Find your soulmate

We, as human being, are social individuals. We feel fulfilled as a person in our society when we have people around us. The right person at your side can double your happiness. We carry within us the desire to communicate, to exchange ideas and feelings. Our life is structured around communication and belonging to a family, a family which offer us membership, protection and offspring.
Isolated persons are prone to mental illness.

What is love?

Love is the feeling that your partner completes your life as a family. Man and woman fall in love because they are two parts of one whole (from society perspective). Man is aggressive and protective, woman is nurturing and demure. So, they complete each other. (NB: each person (woman or man) is complete by him/her self and nobody will interfere with that person inner value. Each person is unique and valuable throughout our humanity).
Love for woman is different from the love for man. For woman perspective the love is beautiful and complete: everything about her lover is perfect and beautiful and a flow of emotional feelings are exploding. Her love will lead her to having sex. For man, love is frustration. He is driven by sexual desires. He love the sexual parts of woman body: the legs, the breasts, the bottom... As for woman love represents empathy (she is there for her man needs: from carrying for her man to showing him affection and consideration), for man love is action (he dominate his woman, protect her and spread his sperm). This is way after sex, woman is loving and in need for caresses, while man is willing to rest and restore his energy to be ready for next action.
However, when a man love the face of his partner, he loves his mother memory.

Is love about looks? You may say so. If a good looking person is appealing to you is because that person look represents the guarantee that he / she is a healthy person with a good chance to produce offspring. Those are the persons with sex appeal and are generally accepted as good looking.
In same category with looks can be included hormonal chemistry between sexual partners. Through hormones are transmitted messages of being sexual potent and available for the person in front of you.
But beauty can incorporate other body characteristic, beside the classical ones. For example that person reminds you about a parental figure, a figure that produce you the familiarity feelings, closeness and offers you comfort and protection. The subject is detailed in the section "How we choose our partner?".
Looks and sexual attraction are not everything in a relationship. Sexual attraction can vanish in time (it is a novelty driven instinct), love is something which should grow in time and consolidate your family through attachment ( it is comfort, security, provides you a pleasant life and happiness). So, another manifestation of love is mental affinity. If the couple has same set of values in life and likes same activities, they can build a future together. (see
Happiness and rational thinking - Pleasant life and Life with meaning).
Admiration is another component of love. When we admire a person, we cherish his / her companionship.
And finally, we are talking about emotional affinity. Feelings are the cause of falling in love.

In our society it is generally accepted that if you love someone, you commit to monogamist relationship with that one. And if you have an affair, it means that you are not in love with your partner anymore. Monogamy is simply an agreement: Woman trades sexual fidelity to man in exchange for protection and his commitment to provide for her and her offspring. Monogamy is a society rule and represents the insurance that the man will spend his life beside his woman and his biological children, he will not provide for other men's children, exception if he adopt existing woman's children. Exceptions are only to enforce the rule.

How to look for him / her when choosing the right person?

First of all, you need to be open for a new relationship. No matter how were your past experiences, everyone can have a good life with the one. You have to be optimist: to want to find the right person, to believe that does exist a soulmate for you and that you can find it.
Mental set is always a must: to experience love, you need to believe in it. Self-esteem is also important: believe that you are worth loving and that you are able to find the right person to love and be loved.
With positive attitude, start a new relationship with sincerity and communication (lack of communication is one of the causes which make a relationship to fail) and give what you want to receive from this relationship (behave with your loved one the way you want her / him to behave with you, be open to your feeling in your relationship).

Learning how to menage your life in two, is not the easiest thing to do: you will see and you will be expose in all kinds of situations pleasant or less pleasant. You need to develop your acceptance: The degree of cleanliness accepted in the couple must be that of the cleanest of them. The degree of humility accepted in the couple must be taken into account by the humblest of them. If these limits are exceeded, frustrations and tensions will be created in the couple and can even lead to the breakup of the couple.
Spend as much time together as possible. Engage both of them in daily activities.
Spend time together doing what you both like to do (hobbies).
Try new activities which appeal to one of you, the other may love it.

Look for similar hobbies and wishes in your life. These will make your life together easy to menage (see Happiness and rational thinking - Pleasant life).

Choose a partner with similar background: social, cultural and economic. If you share similar values, your love will be able to last (see Happiness and rational thinking - Life with meaning).

Choose a partner with similar sexual expectation. Sex is part of your couple life and can create frustration in time (sex as bounding not only procreation). If you wish children, make sure that your partner feels the same way.

Choose the partner who can provide you the life you dream of. If you, as woman, wish a luxuriant life, you should choose a partner financial potent. If you, as men, wish to have home made meals, you should choose a partner with cooking abilities. If you are a party person, you should make sure your partner likes to party as well. And so on...

Choose a partner to be proud of. Admiration is a big thing in relationship. Choose a partner with defects which are not capital for you. It may not be altogether only qualities, but at least you to be able to accept the defects and learn to diminish them into your eyes. Learn to love your partner with good and bad.

It is said that woman is making the decision of getting together into a relationship. Naturally, she will choose a partner financial potent (but not only) to ensure her material well-being for her and her eventual children.

Harmful behaviors in the couple's relationship

Adoration is a behavior which creates rejection because of sick reactions such as abandonment issues.

Psychic anaphrodisiacs (sexual inhibitors):
For men: Thinking of their mother (the most asexual presence in the life of a sexually mature man). Keep away from relationship with a woman which resembles your mother face.
Another powerful psychic anaphrodisiac for men is the presence of another man from the life of his partner (talking about too many details in regards with the performances of the other man in the past of his partner). As woman: keep for yourself the details of your past relationships.
For woman: all the unsafely situations, especially those regarding the future of the relationship will act as psychic anaphrodisiac.

A little routine helps us build a good, strong relationship; but take it too far and it destroys and “kills love.” Finding the balance is up to you.

Confusing behaviors in the couple's relationship

Invariable erotic behavior is manifested through contradictions between verbal and non-verbal language: he/she likes it, then he/she doesn't like it anymore.
Young people may manifest indifference to the person attracted due to the instinct of egos specific to children combined with irresistible attraction to a possible partner.

Beneficial behaviors in the couple's relationship

Jealousy feeling for men is good to cancel the effects of selfishness.

The woman appreciates the compliments related to her beauty, while the man appreciates the compliments related to his deeds.

The place of intimacy between two partners is very important for the woman (she remembers):
- it must be clean and have a bathroom,
- it must provide safety
- and the ambience matters a lot.

To be good in bed means:
- movements that produce a complex of multisensory perceptions (smell (pheromones), skin color, clothes),
- confidence
- and curiosity, spontaneity, the desire to get to know your partner.
If you feel good, you can help your partner to feel good as well. It is a circle. As a woman, you should tell to your partner that he is good in bed.

The expectation theory: Before an event, people are making a "projection" of what will happen. Going on a date, the woman expects something romantic, and the man expects to have sex. It is a conflict situation between the two partners: on the one hand, there is a conflict between her and his expectations, on the other hand, there is a conflict between everyone's expectations and what will happen for real. This conflict situation leads to disappointment.
Men make exaggerated promises to get what they want (the theory of expectations). As men, if you want a harmonious relationship, promise only what will be fulfilled and thus you will enchant the chosen one of your heart.

How we choose our partner?

We have our subconscious formed in our firsts years. In this way, our life is programed in proportion of 95% before we start dating. It is written in our subconscious through our experience at the moment of our childhood.
How we like someone based on our subconscious? We choose to like someone who is familiar to us, maybe someone who reminds us of a parental figure. The person who show us love, who cared for us. That person can be good or bad for us as per the childhood's experiences. So, the life trap is that we might end up in a toxic relationship because of the experiences written in our subconscious. Somebody from our family may caused us traumas which we learn to live with and we are choosing the person who cause us similar traumas in present. This kind of life we know to deal with, it is familiar to us, the lack of trauma is the unknown.
The unknown (good or bad) is what we reject. The familiar, good or bad, we accept because we have a previsions experience, we know how to face it.

When I know I am in a toxic relationship and I need to separate?

Toxic relationship is the one which it creates great and recurrent discomfort to one of the partners in relationship:
- physical or mental or emotional abuse (include traumatic jealousy)
- not having and maintaining same propose in life (include the decision to have children) (see Happiness and rational thinking - Life with meaning).
- promiscuous behavior of one or both partners (adultery)
The first thing in finding a solution for a toxic relationship is to realize that you have a problem and to take decision about it. You may need professional help.

Marriage:

Marriage is the promise and commitment to have a monogamist relationship for the rest of your life. Not all marriages are "for ever", but this "for ever" is the initial intention.
Marriage is usually more appealing to women than to men, because women are driven to have a family (to get the protection and the care from a man) and to have children. The necessity of given birth is in her genes. Almost every woman carries in her mind the idea to became mother in a certain point of her life. Little girls are playing the motherhood role with their dolls and this role will became a directive of her subconscious. The reproductive instinct is very strong and the man should take it into account when propose a woman to marry him.
When men initiate the marriage is often more about commodity of sex than the reproductive instinct. This does not mean that men never want children in their life, but they will delay the moment of being responsible for their kids and to have all the wives attention for themselves. For men, having kids is not always the propose of the marriage.

So, what about having children? There are situations when partners have two different propose of their marriage. One to have children and the other to get sexual commodity. When this is the case, the couple has a big problem. The true way to solve this problem is the divorce: if a child was born during the marriage, mother take the child custody while the father is contributing financial to his child care. If there is no child involved, the woman have to find the right man to start a family with, family which includes children.

It is another situation when marriage occurs: two people which wish to avoid loneliness, to double the fun and happiness in their lives or to find help in each other in difficult moments of life. Usually, it is the case of the late marriage.




Cognitive Schema questionnaire

How to use this questionnaire:

In this questionnaire you will find affirmations that you will use them to describe yourself. Read each affirmation and choose the best answer that define you based on last year. Choose your answer based on your emotional feelings and not based on logic and what you think is correct answer.

1. Read carefully the How to use this questionnaire section.
2. Choose for each affirmation one of the answers below, between 1 to 6, which one fits you best.
3. Write down the question number with the corresponding answers 1 to 6.
4. Compute the total by summarize all the answers.
5. Compare your score with the corresponding threshold. The score over the threshold shows how serious the problem is.
6. Acknowledging the problem you can find how to fix it. Professional help may need it.

Possible answers:

The below questionnaire contains statements used by a person to describe her/himself. Please read each statement carefully and decide how well it describes you. When you are not sure, answer according to what you feel, and not according to what you think is true.

Each statement of these questionnaire has 6 possible answers from 1 to 6.
The answers are (every time in same order for each affirmation):

1. Completely untrue to me
2. Partly untrue to me
3. More true than untrue
4. Mainly true
5. Mostly true about me
6. Describes me perfectly



Questionnaires

Emotional Deprivation (ED)

- the others don’t offer us the nurturance, empathy and protection we need.

1. Usually I didn't have someone to take care of me, to share my life with, or to care much about what was happening to me.

25. Usually, people were not by my side to show me warmth, support and love.

49. For a long time in my life I did not feel that I was special to someone.

73. Most of the time I didn't have someone who really listened to me, who understood me or who invested emotionally in me.

96. Rarely I have had a person to advise me or guide me when I didn't know what to do.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Emotional deprivation ED score:
ED threshold: 7on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Abandonment / Instability (AB)

- the feeling that the close ones will not be able to emotionally support us, or to protect us and they will abandon us in favor of someone better.

2. I clung to those close to me because I was afraid they would leave me.

26. I need the others so much that I'm worried I'll lose them.

50. I am afraid that the people I feel close to will leave me or abandon me.

74. I despair when I feel that someone I care about is moving away from me.

97. Sometimes I am so worried that others will leave me that I send them away from me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Abandonment / instability AB score:
AB threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Mistrust / Abuse (MA)

- the belief that in the end, the others will intentionally hurt, abuse, humiliate, cheat, lie, manipulate, or take advantage on us.

3. I feel that people will take advantage of me.

27. I feel that I must not let my guard down in the presence of others, because otherwise they will intentionally hurt me.

51. It's just a matter of time before someone betrays me.

75. I'm pretty suspicious of others motivations.

98. I am usually vigilant about others motivations.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Mistrust/ abuse MA score:
MA threshold: 11 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective.The score over the threshold shows how serious the problem is.

Social isolation / Alienation(SI)

-the sense that someone is different from others and is not part of any group.

4. I can't find my place anywhere.

28. I am fundamentally different from the others.

52. My place is not here; I'm lonely.

76. I feel alienated from other people.

99. I always feel out of the group.

Calculate your score:
SCORING KEY
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Isolation / alienation SI score:
SI threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.


Defectiveness / Shame (DS)

- the feeling that one is bad, unwanted, inferior, in important respects; or that one would be unlovable to significant others.

5. No man / woman I know can love me once he / she sees my flaws.

29. None of those I want to be with will be able to stay next to me once they get to really know me.

53. I do not deserve the love, attention and respect of others.

77. I feel like I can't be loved.

100. I have too many unacceptable flaws in many areas to let others know me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Defectiveness / shame DS score:
DS threshold: 7 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Failure (FA)

- the belief that one has failed and will inevitably fail in areas of achievement, so he is stupid, lower in status, or less successful than others.

9. In most school or work activities I'm not as good as the others.

33. I am incompetent when it comes to achievements.

57. Most others are more capable than me in terms of professional and achievements.

81. I'm not as talented at work as the others.

103. I'm not as smart as others when it comes to school or work.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Failure FA score:
FA threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Dependence / Incompetence (DI)

- belief that one needs considerable help from others to handle one’s everyday responsibilities in a competent manner.

10. I don't feel able to manage on my own in my daily life.

34. I see myself as a dependent person when it comes to everyday life.

58. I am less capable than most people.

82. My judgment cannot be good in all everyday situations.

104. I do not trust my ability to solve all the problems that arise.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Dependence / incompetence DI score:
DI threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Vulnerability to harm or illness (VH)

- exaggerated fear that imminent illnesses, emotional or external catastrophe will strike at any time and that one will be unable to prevent it.

11. I can't get rid of the feeling that something bad is about to happen.

35. I feel that a disaster (natural, chemical, medical or criminal) can happen at any time.

59. I'm afraid I'll be attacked.

83. I'm afraid I'm going to lose all my money and get poor.

105. I'm afraid I have a serious illness, even though the doctor didn't diagnose anything serious for me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Vulnerability to harm or illness VH score:
VH threshold: 6 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Enmeshment / Undeveloped Self (EM)

- excessive emotional involvement and closeness with one or more significant others (often parents), at the expense of independence and normal social development.

12. I have not been able to separate (walk away) from my parents in the way other people of my age do.

36. My parents and I tend to get involved in each other's lives and problems.

60. It is very difficult for me and my parents to keep intimate secrets from each other without feeling guilty and deceived.

84. I often feel that if my parents live through me, I no longer have a life of my own.

106. I often feel that I do not have a separate identity from my parents or my partner.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Enmeshment / undeveloped self EM score:
EM threshold: 9 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Subjugation (SB)

- suppression of one’s preferences, decisions, desires and suppression of emotional expression, especially anger usually to avoid the abandonment.

13. I think if I do what I feel, I get just in trouble.

37. I feel that I have no choice but to fulfill the wishes of others, otherwise they will reject me.

61. In relationships, I let the other have the last word.

85. I always let others decide for me, so I don't know what I want for myself.

107. It is very difficult for me to ask that to be respected my rights and that to be taken into account my feelings.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Subjugation SB score:
SB threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Self-sacrifice (SS)

- excessive focus on voluntarily meeting the needs of others in daily situations, at the expense of one’s own gratification.

17. I'm the kind of person who usually ends up taking care of those close to me.

41. I am a good person because I think of others more than myself.

65. I am so preoccupied with dealing with the people I care about that I have little time for myself.

89. I've always been the kind of person who listens to the problems of others.

110. Other people know me as doing too much for others and not doing enough for me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Self sacrifice SS score:
SS threshold: 18 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.


Emotional inhibition (EI)

- inhibition of anger, inhibition of positive impulses, difficulty expressing vulnerability or communicating freely about one’s feelings, needs and excessive emphasis on rationality while disregarding emotions.

18. I try too hard to express my positive feelings towards others (affection, concern).

42. I find embarrassing to express my feelings in front of others.

66. I find it hard to show warmth and spontaneity.

90. I control myself so much that people think I have no emotions.

111. People see me as emotionally inflexible.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Emotional inhibition EI score:
EI threshold: 10 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Unrelenting Standards / Hyper-criticality (US)

- the belief that one must strive to meet very high internalized standards, usually to avoid criticism. Its forms are the perfectionism, the excessive attention to details, the rigid rules and the “should”.

19. I have to be the best in everything I do: I don't accept being in second place.

43. I always try to do everything I can / everything that depends on me; I'm not satisfied with "almost good".

67. I have to fulfill all my responsibilities.

91. I feel that there is a constant pressure on me to fulfill and achieve different things.

112. I can't apologize for my mistakes.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Unrelenting standards / hyper-criticality US score:
US threshold: 17 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Entitlement / Grandiosity (ET)

- the belief that one is superior to other people, that claim the right to do or have whatever want, regardless of what is realistic, or the cost to others, all this in order to get control and power.

20. I have problems when I have to accept "no" in response, when I want something from others.

44. I am special and I do not have to accept restrictions imposed by others.

68. I hate being constrained or restrained from what I want to do.

92. I feel that I do not have to follow the rules, norms and conventions that others make.

113. I feel that what I have to offer is more valuable than the contributions of others.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Entitlement / grandiosity ET score:
ET threshold: 14 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Insufficient Self-Control / Self-Discipline (IS)

- the difficulty to practice self-control and discipline to achieve one’s personal goals, or to restrain the excessive expression of one’s emotions and impulses, the excessive desire to maintain the comfort and to avoid unpleasant situations.

21. I can't motivate myself to perform boring and routine tasks.

45. If I can't reach a goal, I quickly become frustrated and give up.

69. I do not sacrifice immediate satisfaction to achieve a distant goal.

93. I can't force myself to do things I don't like, even if I know it's for my own good.

114. I was rarely able to rely on my own decisions.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Insufficient self-control / self-discipline IS score:
IS threshold: 12 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Approval-Seeking / Recognition-Seeking (AS)

- excessive emphasis on gaining approval, recognition, attention from other people, the one’s sense of esteem is dependent on the reactions of others.

6. It is important for me to be liked by almost everyone I know.

14. I change depending on the people I am with, so that they like me more.

22. I'm trying to adapt.

30. My self-esteem is mostly based on how others see me.

38. Having money and knowing a lot of "good" people makes me more valuable.

46. I invest a lot of time in the way I look, so that everyone around me can appreciate me.

54. My own achievements are more valuable to me if people notice them.

62. I'm so preoccupied with getting used to it that sometimes I forget who I am.

70. I find it difficult to set my own goals without thinking about how others will react to my choices.

78. When I think about the decisions in my life, I realize that I made most of them with the approval of others.

86. Even if I don't like someone, I still want him / her to like me.

94. When I don't get much attention from others, I feel unimportant.

101. I seek recognition and admiration when I express my opinion at a meeting or gathering.

108. Numerous compliments and rewards make me feel valuable.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Approval-Seeking / Recognition-Seeking AS score:
AS threshold: 35 on a scale from 0 to 84.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Negativity / Pessimism (NP)

– an excessive focus on the negative aspects of life and minimizing or neglecting the positive aspects.

7. Even when things seem to be going well, I think it's only temporary.

15. If something good happens sometimes, I'm afraid something bad will happen.

23. You can't always be careful enough; something bad will always happen.

31. No matter how hard I work, I'm afraid I might run out of money.

39. I'm worried that a wrong decision can lead to disaster.

47. I am often obsessed with minor decisions because the consequences of a mistake can be serious.

55. I feel better pretending that things will not go well for me, so that I don't feel bad if things don't really go well.

63. I focus mainly on negative events and life situations.

71. I tend to be pessimistic.

79. People close to me think I'm too worried.

87. If people get excited about something, I feel uncomfortable and feel the need to warn them that something bad is going to happen.


Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Negativity / Pessimism NP score:
NP threshold: 21 on a scale from 0 to 66.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Punitiveness (PU)

– the belief that people should be punished for making mistakes.

8. If I make a mistake, I deserve to be punished.

16. If I don't do everything that depends on me, I can expect to lose.

24. There is no excuse if I'm wrong.

32. People who do not know their limits should be punished.

40. I generally do not accept the apologies of others. They are not willing to take responsibility and bear the consequences.

48. If I don't do my job, I should suffer the consequences.

56. I often think about the mistakes I make and I am angry with myself.

64. When people do something wrong, I have trouble applying the "forgive and forget" principle.

72. I can't forgive even if the person has apologized.

80. I get upset when I think someone gave up something too quickly.

88. I get annoyed when people apologize and blame others for their problems.

95. It doesn't matter why I'm wrong; when I did something wrong, I have to pay.

102. I blame myself for the things I failed at.

109. I am a bad person who deserves to be punished.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Punitiveness PU score:
PU threshold: 36 on a scale from 0 to 84.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.


References:

Chestionar YSQ - 114 intrebari

NEW ENTRIES

Basics data process

Study notes

Types of data
  • Structured
    table-based source systems such as a relational database or from a flat file such as a comma separated (CSV) file
    The primary element of a structured file is that the rows and columns are aligned consistently throughout the file.
  • Semi-structured
    data such as JavaScript object notation (JSON) files, which may require flattening prior to loading into your source system.
    When flattened, this data doesn't have to fit neatly into a table structure.
  • Unstructured
    data stored as key-value pairs that don't adhere to standard relational models and Other types of unstructured data that are commonly used include portable data format (PDF), word processor documents, and images.
Types of data by usage
  • Operational data
    Usually transactional data that is generated and stored by applications, often in a relational or non-relational database.
  • Analytical data
    Data that has been optimized for analysis and reporting, often in a data warehouse.
  • Streaming data
    Perpetual sources of data that generate data values in real-time, often relating to specific events.
    Common sources of streaming data include internet-of-things (IoT) devices and social media feeds.
  • Data pipelines
    Used to orchestrate activities that transfer and transform data.
    Pipelines are the primary way in which data engineers implement repeatable extract, transform, and load (ETL) solutions that can be triggered based on a schedule or in response to events.
  • Data lakes
    Storage repository that holds large amounts of data in native, raw formats
    Data lake stores are optimized for scaling to massive volumes (terabytes or petabytes) of data.
  • Data warehouses
    Centralized repository of integrated data from one or more disparate sources.
    Data warehouses store current and historical data in relational tables that are organized into a schema that optimizes performance for analytical queries.
  • Apache Spark
    Parallel processing framework that takes advantage of in-memory processing and a distributed file storage. It's a common open-source software (OSS) tool for big data scenarios.
Data operations
  • Data integration
    Establishing links between operational and analytical services and data sources to enable secure, reliable access to data across multiple systems.
  • Data transformation
    Operational data usually needs to be transformed into suitable structure and format for analysis
    It is often as part of an extract, transform, and load (ETL) process; though increasingly a variation in which you extract, load, and transform (ELT) the data is used to quickly ingest the data into a data lake and then apply "big data" processing techniques to transform it. Regardless of the approach used, the data is prepared to support downstream analytical needs.
  • Data consolidation
    Combining data that has been extracted from multiple data sources into a consistent structure - usually to support analytics and reporting.
    Commonly, data from operational systems is extracted, transformed, and loaded into analytical stores such as a data lake or data warehouse.


  1. Operational data is generated by applications and devices and..
  2. Stored in Azure data storage services such as Azure SQL Database, Azure Cosmos DB, and Microsoft Dataverse.
  3. Streaming data is captured in event broker services such as Azure Event Hubs.

  1. Operational data must be captured, ingested, and consolidated into analytical store and ...
  2. From where it can be modeled and visualized in reports and dashboards.
These tasks represent the core area of responsibility for the data engineer.
The core Azure technologies used to implement data engineering workloadsinclude:
  • Azure Synapse Analytics
    Azure Synapse Analyticsincludes functionality for pipelines, data lakes, and relational data warehouses.
  • Azure Data Lake Storage Gen2
  • Azure Stream Analytics
  • Azure Data Factory
  • Azure Databricks
The analytical data stores that are populated with data produced by data engineering workloads support data modeling and visualization for reporting and analysis, often using sophisticated visualization tools such as Microsoft Power BI.

Azure Data Lake Storage Gen2
Provides a cloud-based solution for data lake storage in Microsoft Azure, and underpins many large-scale analytics solutions built on Azure.
A data lake is a repository of data that is stored in its natural format, usually as blobs or files. Azure Data Lake Storage is a comprehensive, massively scalable, secure, and cost-effective data lake solution for high performance analytics built into Azure.

  • Hadoop compatible access.
    You can store the data in one place and access it through compute technologies including Azure Databricks, Azure HDInsight, and Azure Synapse Analytics
  • Security
    Data Lake Storage supports access control lists (ACLs) and Portable Operating System Interface (POSIX) permissions that don't inherit the permissions of the parent directory
  • Performance
  • Data redundancy

  • Blob
    in terms of blob manageability the blobs are stored as a single-level hierarchy in a flat namespace.
    Flat namespaces, by contrast, require several operations proportionate to the number of objects in the structure.
  • Azure Data Lake Storage Gen2
    builds on blob storage and optimizes I/O of high-volume data by using a hierarchical namespace that organizes blob data into directories, and stores metadata about each directory and the files within it.
    Hierarchical namespaces keep the data organized, which yields better storage and retrieval performance for an analytical use case and lowers the cost of analysis.
Stages for processing big data solutions that are common to all architectures:
  1. Ingest
    The ingestion phase identifies the technology and processes that are used to acquire the source data. This data can come from files, logs, and other types of unstructured data that must be put into the data lake.
    The technology that is used will vary depending on the frequency that the data is transferred.
    1. Batch movement of data, pipelines in Azure Synapse Analytics or Azure Data Factory
    2. Real-time ingestion of data, Apache Kafka for HDInsight or Stream Analytics .
  2. Store
    The store phase identifies where the ingested data should be placed. Azure Data Lake Storage Gen2 provides a secure and scalable storage solution that is compatible with commonly used big data processing technologies.
  3. Prep and train
    The prep and train phase identifies the technologies that are used to perform data preparation and model training and scoring for machine learning solutions.
    Common technologies that are used in this phase are Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and Azure Machine Learning.
  4. Model and serve
    Involves the technologies that will present the data to users. These technologies can include visualization tools such as Microsoft Power BI, or analytical data stores such as Azure Synapse Analytics. Often, a combination of multiple technologies will be used depending on the business requirements.
Use Azure Data Lake Storage Gen2 in data analytics workloads:
  • Big data processing and analytics
    Usually refer to analytical workloads that involve massive volumes of data in a variety of formats that needs to be processed at a fast velocity - the so-called "three v's".
    Big data services such as Azure Synapse Analytics, Azure Databricks, and Azure HDInsight can apply data processing frameworks such as Apache Spark, Hive, and Hadoop.
  • Data warehousing
    Integrate large volumes of data stored as files in a data lake with relational tables in a data warehouse.
    There are multiple ways to implement this kind of data warehousing architecture. The diagram shows a solution in whichAzure Synapse Analytics hosts pipelines to perform extract, transform, and load (ETL) processes using Azure Data Factory technology.
  • Real-time data analytics
    streaming data requires a solution that can capture and process a boundless stream of data events as they occur.
    Streaming events are often captured in a queue for processing. There are multiple technologies you can use to perform this task, including Azure Event Hubs as shown in the image.
    Azure Stream Analytics enables you to create jobs that query and aggregate event data as it arrives, and write the results in an output sink
  • Data science and machine learning
    Involves the statistical analysis of large volumes of data, often using tools such as Apache Spark and scripting languages such as Python. Azure Data Lake Storage Gen 2 provides a highly scalable cloud-based data store for the volumes of data required in data science workloads.
Azure Synapse Analytics
Analytical technique that organizations commonly use:
  • Descriptive analytics
    which answers the question “What is happening in my business?”. The data to answer this question is typically answered through the creation of a data warehouse in which historical data is persisted in relational tables for multidimensional modeling and reporting.
  • Diagnostic analytics,
    which deals with answering the question “Why is it happening?”. This may involve exploring information that already exists in a data warehouse, but typically involves a wider search of your data estate to find more data to support this type of analysis.
  • Predictive analytics,
    which enables you to answer the question “What is likely to happen in the future based on previous trends and patterns?
  • Prescriptive analytics,
    which enables autonomous decision making based on real-time or near real-time analysis of data, using predictive analytics.
Azure Synapse Analytics provides a cloud platform for all of these analytical workloads through support for multiple data storage, processing, and analysis technologies in a single, integrated solution.

To support the analytics needs of today's organizations, Azure Synapse Analytics combines a centralized service for data storage and processing with an extensible architecture through which linked services enable you to integrate commonly used data stores, processing platforms, and visualization tools.

A Synapse Analytics workspace defines an instance of the Synapse Analytics service in which you can manage the services and data resources needed for your analytics solution.
A workspace typically has a default data lake, which isimplemented as a linked serviceto an Azure Data Lake Storage Gen2 container.
Azure Synapse Analytics includes built-in support for creating, running, and managing pipelines that orchestrate the activities necessary
  • to retrieve data from a range of sources,
  • transform the data as required, and
  • load the resulting transformed data into an analytical store.
Azure Synapse Analytics supports SQL-based data querying and manipulation through two kinds of SQL pool that are based on the SQL Server relational database engine:
  • A built-in serverless pool that is optimized for using relational SQL semantics to query file-based data in a data lake.
    use the built-in serverless pool for cost-effective analysis and processing of file data in the data lake
  • Customdedicated SQL pools that host relational data warehouses.
    use dedicated SQL pools to create relational data warehouses for enterprise data modeling and reporting.
Processing and analyzing data with Apache Spark
In Azure Synapse Analytics, you can create one or more Spark pools and use interactive notebooks to combine code and notes as you build solutions for data analytics, machine learning, and data visualization.

Exploring data with Data Explorer
Data Explorer uses an intuitive query syntax named Kusto Query Language (KQL) to enable high performance, low-latency analysis of batch and streaming data.

Azure Synapse Analytics can be integrated with other Azure data services for end-to-end analytics solutions. Integrated solutions include:
  • Azure Synapse Link
    enables near-realtime synchronization between operational data in Azure Cosmos DB, Azure SQL Database, SQL Server, and Microsoft Power Platform Dataverse and analytical data storage that can be queried in Azure Synapse Analytics.
  • Microsoft Power BI integration
    enables data analysts to integrate a Power BI workspace into a Synapse workspace, and perform interactive data visualization in Azure Synapse Studio.
  • Microsoft Purview integration
    enables organizations to catalog data assets in Azure Synapse Analytics, and makes it easier for data engineers to find data assets and track data lineage when implementing data pipelines that ingest data into Azure Synapse Analytics.
  • Azure Machine Learning integration
    enables data analysts and data scientists to integrate predictive model training and consumption into analytical solutions.
Across all organizations and industries, the common use cases for Azure Synapse Analytics are identified by the need for:
  • Large-scale data warehousing
    Data warehousing includes the need to integrate all data, including big data, to reason over data for analytics and reporting purposes from a descriptive analytics perspective, independent of its location or structure.
  • Advanced analytics
    Enables organizations to perform predictive analytics using both the native features of Azure Synapse Analytics, and integrating with other technologies such as Azure Machine Learning.
  • Data exploration and discovery
    The serverless SQL pool functionality provided by Azure Synapse Analytics enables Data Analysts, Data Engineers and Data Scientist alike to explore the data within your data estate. This capability supports data discovery, diagnostic analytics, and exploratory data analysis.
  • Real time analytics
    Azure Synapse Analytics can capture, store and analyze data in real-time or near-real time with features such as Azure Synapse Link, or through the integration of services such as Azure Stream Analytics and Azure Data Explorer.
  • Data integration
    Azure Synapse Pipelines enables you to ingest, prepare, model and serve the data to be used by downstream systems. This can be used by components of Azure Synapse Analytics exclusively.
  • Integrated analytics
    With the variety of analytics that can be performed on the data at your disposal, putting together the services in a cohesive solution can be a complex operation. Azure Synapse Analytics removes this complexity by integrating the analytics landscape into one service. That way you can spend more time working with the data to bring business benefit, than spending much of your time provisioning and maintaining multiple systems to achieve the same outcomes.

Cognitive service terms

Personalizer
Cognitive service for a decision support solution.
Analyzes user's real-time behavior, for example, online shopping patterns, and then help your app to choose the best content items to show.

Spatial Analysis
Cognitive service for a computer vision solution.
Ingest a live video stream, detect and track people, monitor specific regions of interest on the video, and generate an event when a specific trigger occurs.
Can monitor an area in front of the checkout counter and trigger an event when the count of people exceeds a defined number.

QnA Maker
Cognitive service for a Natural Language Processing (NLP) solution.
Helps to build a custom knowledge base to provide a natural conversation layer over your common questions and answers.

Anomaly Detector
Finds data that is an outlier or is out of trend in time-series data. It does not analyze content supplied by users for offensive content such as adult, racy, or
gory Images.

Content Moderator
Cognitive service that identifies content that is potentially offensive, including images that may contain adult, racy, or gory content. It flags such content automatically for a human to review in a portal.

Smart Labeler
Used in training models (object identification).
Tag uploaded images automatically, reducing the manual effort needed to improve the model. You must check and adjust tags.

Normalizepunctuation
Language Understanding - remove punctuations such as dots, commas, brackets, and others from your utterances before your model gets trained or your app's endpoint queries get predicted. However, it does not eliminate the effect of accented characters.

NormalizeDiacritics
Language Understanding - replace accented characters, also known as diacritics, with regular characters.
Allows you to ignore the effect of diacritics during your app's training and prediction.
Only available for the supported languages like Spanish, Portuguese, Dutch, French, German, and Italian.

Phrase list
Language Understanding - list of similar words or phrases that can be used by your model as a domain-specific vocabulary.
Example in travel industry: Single, Double, Queen, King, and Twin as a phrase list feature, so the app can recognize from the utterances preferences for a hotel room type.
The phrase list feature can improve the quality of your NLU app understanding of intents and entities

filterable
QnA - Property specifies if the field in the index can be used as a filter and can restrict the list of documents returned by the search.
The filterable property is a Boolean value defined on a field in the index.

sortable
QnA - allows other fields to be used for sorting results
By default, search results are ordered by score.

facetable
QnA - returns a hit count by category, for example the number of results by test type.

retrievable
QnA - Boolean; if set to true, includes the field in the results of the search, if set to false the field will not be included in the search result.
Does not allow the user search on the field or restrict by the value of the field.

Knowledge store
Azure Cognitive Search - place in Azure Storage, where is stored data created by a Cognitive Search enrichment pipeline.
It is used for independent analysis or downstream processing in non-search scenarios like knowledge mining.
It is an enriched (created/generated by the skillset) content stored:
  • tables TAB3 (key phrase extraction)
    table projections require the data to be mapped to the knowledge store using outputFieldMappings with:
    • sourceFieldName
    • targetFieldName
  • blob containers in storageContainer.
When you save enrichments as a table projection you need to specify:
  • source - path to projection
  • tableName - name of table in Azure Table storage

Projections
Enriched documents from Cognitive Services that are stored in a knowledge store.
Enhance and shape the data.
Type of projections:
  • File (images )
  • Object (JSON)
  • Table (dictionary)
Skillset
Enrichment process of Cognitive Search enrichment pipeline.
Move a document through a sequence of enrichments that invoke atomic transformations, such as recognizing entities or translating text.
Output:
  • always a search index.
  • can be projections in a knowledge store.
Search index and knowledge store are mutually exclusive products of the same pipeline.
They are derived from the same inputs but their content is structured, stored, and used in different applications.

encryptionKey
Azure Cognitive Search, enrichment - optional and used to reference an Azure Key Vault for the skillset, not for the knowledge store.

referenceKeyName
Azure Cognitive Search, enrichment - used to relate data across projections.
If it is not specified, then the system will use generatedKeyName

fieldMappings
Property is used to map key fields.
It is optional. By default, the metadata_storage_path property is used.

storageConnectionString
Required when storing the skillset's output data into a knowledge store (not required for the indexer)

cognitiveServices
Defines the Cognitive Services resource to use to enrich the data
Required when defining the skillset (not required for the indexer).

LUISGen
Command-line tool that can generate C# and Typescript classes for your LUIS intents and entities.

Lodown
Command-line tool that you use to parse .lu files.
If the file contains Intents, entities, and patterns, LUDown tool can generate a (LUIS) model in JSON format.
If the file contains question and answer pairs, then the LUDown can generate a knowledge base in JSON format.

Chatdown
Command-line tool that can be used to generate mock conversations between a user and a bot.
Expects input in a chat file format to generate conversation transcripts in .transcript format that can be consumed by the Bot Framework Emulator.
->Work with:Bot Framework Emulator and ngrok

ngrok
1. Tool that allows you to expose a web server running on your local computer to the internet.
It helps you to locally debug a bot from any channel.
2. It is integrated with Bot Framework Emulator and allows Bot Framework Emulator to connect to remote endpoints such as the production Web app running in Azure.
Enables Bot Framework Emulator to bypass the firewall (tunnel) on your computer and connect to the Azure bot service and intercept the messages to and from the bot.
->Work with: Bot Framework Emulator and Chatdown

Bot Framework Emulator
Tool to debug your bot.
Bot Framework Emulator is a Windows desktop application that can connect to a bot and inspect the messages sent and received by the bot.
Framework Emulator to connect to remote endpoints such as the production Web app running in Azure.
Can view conversation transcripts (.transcript files) and can use these transcript files to test the bot.
Conversations between a user and a bot can be mocked up as text files in markdown in the .chat file format.
->Work with: Bot Framework Emulator and Chatdown

Active learning
If enabled, QnA Maker will analyze user queries and suggest alternative questions that can improve the quality of your knowledge base.
If you approve those suggestions, they will be added as alternative questions to the knowledge base, so that you can re-train your QnA Maker model to serve your customers better.

Chit-chat
Pre-built data sets.
The chit-chat feature will add a predefined personality to your bot to make it more conversational and engaging.

Precise answering
The precise answering feature uses a deep learning model to identify the intent in the customer question and match it with the best candidate answer passage from the
knowledge base.

Managed keys
Encryption keys managed by Microsoft or the customer used to protect QnA Maker's data at rest.

Regular expression type
Uses a regular expression (Regex) to search for a pattern. It is used to match fixed patterns in a string such as numbers with two decimal places.

Default recognizer type
It includes LUIS and QnA Maker recognizers.

Custom recognizer type
Allows you to define your own recognizer - JSON format
It may be possible to perform regular expressions using a custom recognizer in JSON, but this requires additional effort to define and test the JSON to extract the numbers.

Orchestrator recognizer type
Allows you to link other bots to your bot as skills. It may help to find patterns in text strings but with more effort and resources.

Computer Vision
Only identifies well-known brands

Object Detection
Can locate and identify logos in images. Custom Vision resource in Azure must exist.

Classification project
Classification model for Custom Vision analyze and describe images.

Partitions
Cognitive search - Control the distribution of index across the physical storage.
Partitions split data across different computing resources. This has the effect of improving the performance of slow and large queries.
For example, with three partitions, you divide your index into three slices. To meet the

Replicas
Primarily used for load balancing, and so assist with the response for all queries under load from multiple users.
Adding a replica will not make an individual query perform faster.
Cognitive search - Microsoft guarantees 99.9% availability of read-write workloads for queries and Indexing if your Azure Cognitive Search resource has three or more replicas.

Sample labeling tool
Tool for training custom Form Recognizer.

Azure Files
Are fully managed file shares that you can mount in Windows, Linux, or macOS machines.

Custom Vision service
Allows you to train image classification and object detection algorithms that you can use in your image recognition solutions.

Azure Video Analyzer for Media
Accessible athttps://wwwvideoindexer_ai.
With a free trial, you can use up to 600 minutes of free video indexing using the Video Analyzer for Media website or up to 2,400 minutes when accessing it through API.
The Content model customization option allows you to manage Person, Brands, and Language models, for example, to add custom faces or exclude certain brands.

endpoint.microsoft.com
Microsoft Intune portal address. Manage your mobile devices, deploy device policies, and monitor them for compliance.

azure-cognitive-services/decision repository
Common storage location for Azure Cognitive Services container images in the Decision domain, for example Anomaly Detector.

azure-cognitive-services/textanalytics repository
Common storage location for Text Analytics container images such as Key Phrase Extraction or Text Language Detection.

food(compact) domain
Allows you to classify photographs of fruit and vegetables.
Compact models are lightweight and can be exported to run on edge devices.

food domain
Will help you classify photographs of fruit and vegetables.
It is not optimized to run on edge devices.
Cannot be exported from the Custom Vision portal for offline use.

retail(compact) domain
Optimized for images that are found in a shopping catalog or shopping website.
Us it for high precision classification between dresses, pants, shirts, etc.

products on shelves domain
Object detection domain that can detect and classify products on shelves.
It is not optimized to run on edge devices.
It cannot be exported from the Custom Vision portal for offline use.

Adaptive expressions
Used by language generation to evaluate conditions described in language generation templates.

Language generation
Enables your bot to respond with varying text phrases, creating a more natural conversation with the user.

Language understanding
Enables your bot to understand user input naturally and to determine the intent of the user.

Skills
Allow you to call one bot from another and create a seamless bot experience for the user.

Orchestrator
Combines your bot with other bots as skills and determines which bot to use to respond to the user.

Verify API
Allow a person automated entry to the premises when showing their face to the entrance gate camera.
Determines whether the face belongs to that same person.
The Face Verify API will compare the person's image against the enrolled database of persons' images and provide them access, creating a one-to-one mapping between the two images to verify if both images belong to the same person.

Detect API
Generate engagement reports for using student emotions and head poses during period of time.
Face detection captures a number of face-related attributes like head pose, gender, age, facial hair, etc.
Admins can use this JSON output from the images to study the people engagement.

Identify API
Identify persons who are attending a specific event.
Face identification allow admins to compare given event photos against all previous photographs of the
same subject and do a one-to-many mapping.

Group API
Face grouping divides a set of unknown faces into smaller sets of groups based on similarity.
Additionally, it also returns a set of face IDs having no similarities.
A single person can have multiple groups, although each returned group is likely to belong to the same person.
These different groups of the same person are differentiated due to additional factors like expression.

Precision
It is about predicted. Indicates what is the proportion of true positives (TP) over the sum of all TP and False Positives (FP).
It uses the formula: TP/ (Tp Fp)

Recall
Indicates the fraction of actual classifications that were correctly identified.
It uses the formula: TP / (TP + FN)

Roles
Are added to entities to distinguish between different contexts.
Example: flight origin and flight destinatiom - you can add roles to the prebuilt geographyV2 entity.

Features
Provide hints for interchangeable words. Features act as synonyms for words when training a LUIS app.

Pattern
Patterns are used with entities and roles to extract information from an utterance.

synonym map
Resource that supplements the content of a search index with equivalent terms.
It cannot be used to enable a search-as-you-type functionality

Knowledge store - object or file projections
With object and file projections, you can write the content from source documents, skills output, and enrichments to blob storage.

Bot Framework Composer
Ca be published as web app to the Azure App Service
Run as a serverless application In the Azure Functions.

Skill bot
Boot that can perform tasks for another bot.
Skill manifest is required for bots that are skills

Skill consumer bot
Bot that can call other bots as skills

skill manifest
JSON file that describes the actions that it exposes to the skill consumer and the parameters it requires.

Billable Cognitive Search
Must be used for the Azure scenarios where you expect high or frequent load.
As an all-in-one resource, Azure Cognitive Service provides access to Computer Vision, Text Analytics, and Text Translation services through the relevant endpoints.
The Computer Vision service particularly provides OCR the capability to identify and extract text from given images.

Image Analysis
Can analyze the image content and generate captions and tags or identify celebrities and landmarks.
For the low-volume text extraction, you can use built-in OCR skills, as it allows the processing of a limited number of documents for free.

built-in Key Phrase Extraction
Can evaluate given input text and return a list of detected key phrases.

Faceted navigation in Azure Cognitive Search
Filtering feature that enables drill-down navigation in your search-enabled application.
Rather than typing your search expression, you can use faceted navigation to filter your search results through the exposed search criteria such as range or counts within your Azure Cognitive Search index.

Brands model
Identify mentions of products, services, and companies.
By using content model customization, you can configure the Brands model to identify Bing suggested brands or any other brands that you add manually.
For example, if Microsoft is mentioned in video or audio content or if it shows up in visual text in a video
Video Analyzer for Media detects it as a brand in the content.

Person model
Recognize celebrities from video content.
By using content model customization, you can configure the Person model to detect celebrity faces from a database of over one million faces powered by various data sources like Internet Movie Database (IMDB), Wikipedia, and top Linkedln influencers.

Language model
Ability to determine industry terms or specific vocabulary.
By using content model customization, you can configure the Language model to add your own vocabulary or industry terms that can be recognized by the Video Analyzer for Media.


cognitiveservices_azure.com domain
Can be used to access the Computer Vision API and generate the required image thumbnails. Example:
https://MYAPPLOCATION.cognitiveservices_azure.com/vision/v3_1/generateThumbnail
https://MYAPPNAME.api_cognitive_microsoft.com/vision/v3_1/generateThumbnail

azurewebsites_net
Subdomain reserved for the use of Azure web apps.
This subdomain is assigned to your custom web apps that you deploy in Azure.

azure-api.net
Subdomain is reserved for use by Azure API Management instances.
API Management can potentially hide Computer Vision endpoints to act as a frontend Interface.

Azure Application Insights
Helps in monitoring your container across multiple parameters like availability, performance, and usage. It is a recommended, but optional, setting when configuring the docker container.
It is not a required setting that must be configured for telemetry support of your container.

Direct Line speech
Channel that allows users to interact with your bot via voice.
Uses the Cognitive Service Speech-to-Text service.

Custom commands
Used with voice assistants for more complex task-orientated conversations.
Use speech-to-text to transcribe the user's speech, then take action on the natural language understanding of the text.

Language understanding
Enables your bot to understand user input naturally and to determine the intent of the user.

Language generation
Use templates enable you to send a variety of simple text messages to users.

Telephony
Channel that allows users to interact with the bot over the phone.
Only enables voice over the telephone not on other channels.

Indexer
Crawler that extract searchable text and respective metadata from an external Azure data source.
It populates a search index mapping between source data and your Azure Cognitive Search index.
Supported as a data source:
  • Azure Table Storage
  • Azure Data Lake Gen2
  • Azure Cosmos DB
Azure File Storage
Provides files shares in cloud that are fully managed and accessible through the SMB or NFS protocol.

Azure Data Lake Gen1
Designed for big data analytic workloads and acts as an enterprise-wide hyper-scale repository.

Azure Bastion
Enables secure RDP (Remote Desktop Protocol) or SSH (Secure Shell) connectivity to your VM, without the need for the VM to have a public IP address.

HeroCard
Single large image and one or more buttons with text.
It has an array of card images and an array of card buttons.
The buttons can either return the selected value to the bot or open a url.

CardCarousel
Collection of cards that allows your user to view horizontally, scroll through, and select from.
The code would need to specify an attachmentLayout of carousel in order to display as a carousel.

ReceiptCard
Contains a list of Receiptltem objects with a button at the end.
You should not use SuggestActions_ SuggestActions displays a list of suggested actions. It uses an array of
card actions.

ImBack
Shows the accept button. The Imback activity type sends a message to the bot when the user selects the button containing the value specified in the parameters.

openlJrl
Show the a webpage.
An openlJrl activity type Opens the URL specified in the value parameters, which in this case is the organization's privacy policy.
You should not use Signin. The Signin activity type uses OAuth to connect securely with other services.

displayText
Used with the messageBack activity type to display text in the chat. It is not sent to the bot.

Azure Form Recognizer
Service that allows you to analyze and extract text from structured documents like invoices.
Azure Cognitive Search does not provide any built-in skills to apply the Form Recognizer's functionality in an Al enrichment pipeline.
For this reason, you need to create a custom skill for it.

Bing Entity Search
Used to describe given geographical locations.
The Bing Entity Search functionality is not available in Azure Cognitive Search as a built-in cognitive skill.
For this reason, you need to build a custom skill and programmatically call the Bing Entity Search API to return descriptions for the given locations.

management_azure_com
Endpoint for the management of Azure services including the Search service
It enables the management and creation of keys for the Search service. Here you create the key for your search app - YOUR_COG_SERACH

YOUR_COG_SERACH.search.windows_net
Endpoint for the Search service. The client application will use this to query the indexes,

api.cognitive.microsoft_com
Endpoint is used by the Bing Search services.

Microsoft.Search
Provider for the Search service.
Using this provider, you can regenerate the admin keys or create query keys for the Search service.

Microsoft.CognitiveServices
Provider for generic Cognitive Services.
It can regenerate the primary and secondary keys for the service, but it cannot generate query keys for the Search service.

Microsoft_Authorization
Provider for managing resources in Azure and can be used to define Azure policies and apply locks to resources.


SpeechRecognizer class
Start the speech service.
Can perform speech-to-text processing.

AudioDataStream
Represents an audio data stream when using speech synthesis in speech-to-text.

addPhrase
Add the ITEM_NAME_THAT_HAS_TO_BE_CONV_TO_TEXT to improve recognition of the product name.

start_continuous_recognition
Starts speech recognition until an end event is raised.
Continuous recognition can easily process the audio up to 2 min long

recognize_once / recognize_once_async
Methods only listen to audio for a maximum of 15 seconds.


Upload
Method of the IndexDocumentsAction class that you can use to push your data to a search index.
If the document is new, then it is inserted. If the document already exists, then this method updates its values instead.

IndexDocuments
Method of the SearchClient class that you can use to send a batch of upload, merge, or delete actions to your target search index.

Merge
Method of the IndexDocumentsAction class that you can use to update the values of an existing document. The
Merge method will fail if you will try to push data into a document that does not yet exist in the search index.

Autocomplete
Method of the SearchClient class that you can use to use the search-as-you-type functionality in your app.
It uses text from your input field to autosuggest query terms from the matching documents in your search index.

GetDocument
Method of the SearchClient class that you can use to retrieve the specific document from your search index. In your case, you need to upload new documents instead.

customEvents
Telemetry sent by your bot to Azure Application Insights can be retrieved by the Kusto query only from the customEvents table.
Analyze your bot's telemetry

summarize operator
Produces a table with the data aggregated by specified columns.
This operator can call the count() aggregation function to return a count of the group.

Heartbeat table
Azure Monitor collects heartbeat data from Azure resources like virtual machines (VMs).

StormEvents table
Table in the Azure Data Explorer's sample database that contains information about US storms.

top operator
Returns the first N records sorted by the specified column

Bot Framework Composer
Windows desktop application that allows you to build bots using visual designer interface.

Log Analytics workspace
Used to send logs to a repository where they can be enriched with other monitoring logs collected by Azure Monitor to build powerful log queries.
Log Analytics is a flexible tool for log data search and analysis.
In a Log Analytics workspace you can combine your Azure Cognitive Services logs with other logs and metrics data collected by Azure Monitor, use Kusto query language to analyze your log data and also leverage other Azure Monitor capabilities such as alerts and visualizations.

Event Hub
Stream data to external log analytics solutions.
Event Hub can receive, transform and transfer millions of events per second.
With Event Hub, you can stream real-time logs and metrics from your Azure Cognitive Services to external systems such as Security Information and Event Management (SIEM) systems or third-party analytics solutions.

Azure Blob storage
Used to archive logs for audit, static analysis or backup purposes, keeping them in JSON format files.
Blob storage is optimized for storing big volumes of unstructured data.
With Blob storage you can keep your logs in a very granular format by the hour or even minutes, to assist with a potential investigation of specific incidents reported for your Azure Cognitive Services.

PythonSettings.json
It is a workspace configuration file for the Python used in the Visual Studio integrated development environment (IDE).

requirements.txt
In Python, you can use requirements.txt to specify the external packages that your Python project requires. You can install them all using the Python pip installer.

Cloning model
Copies the model into a new version.
The cloned version becomes the active version.
Cloning allows you to make changes to the model, such as adding intents, entities, and utterances, and test them without changing the original version of the model

Pattern.Any entity.
Helps the model understand the importance of the word order.
It is a variable-length placeholder to indicate where the entity starts and ends in a sentence.
It is used when utterances are very similar but refer to different entities and when entities are made up of multiple words.

activity handler
Send a welcome message when a user joins the bot conversation.
An activity handler organizes the conversational logic for a bot. Activity handlers respond to events such as a user joining the bot conversation.

component dialog
Use it to create a reusable set of steps for one or more bots.
A component dialog is a set of dialogs that can call the other dialogs in the set.
The component dialog manages the child dialogs in this set.
A component dialog can be reused in the same bot or in several bots.

waterfall dialog
Us it to create a set of sequential steps for a bot
A waterfall dialog is a set of dialog steps in a sequence to gather specific information from a user.

prompt dialog
Single individual prompt and user response.
Component dialogs and waterfall dialogs will contain one or more prompt dialogs.

Chat files
Are markdown files. They consist of two parts:
  • header - here you define participants
  • conversation.
Person
Object you add to the Face API to store names and images of faces for identification.
A Person can hold up to 248 face images.

PersonDirectory
Structure into which you add the Persons and their facial images.
PersonDirectory can hold up to 75 million Persons and does not need training for new facial images.

DynamicPersonGroup
Subset of identities from a PersonDirectory that you can filter identification operations on.
Using a DynamicPersonGroup, you can increase the accuracy of facial identification by only verifying faces against the smaller list people, instead of the entire set of faces in the PersonDirectory.

FaceList
Used for the Find Similar operation, not for identification.
Find Similar is used to find people who look similar to the facial image, it cannot be used to verify that a face belongs to a person.

PersonGroup
Can only hold 1,000 Persons on the free tier and 10,000 Persons on the standard tier.

LargePersonGroup
Can hold up to one million identities.
However, a LargePersonGroup requires training when a new facial image is added.
This training can take 30 minutes, and the face cannot be recognized until training is complete.
This solution unable to identify new faces immediately

Direct Line channel
Does not enable voice in capability in your bot.
Enables the communication between a client app and your bot over HTTPS protocol.
Does not support support network isolation

Direct Line Speech
Enables voice in or voice out capabilities in your bot
Does not support support network isolation

Direct Line App Service Extension
Ensure that traffic between your bot and client applications never leaves the boundaries of Azure VNet

You should use pattern matching or LUIS. The Speech software development kit (SDK) has two ways to
. The first is

pattern matching
Can be used to recognize intents for simple instructions or specific phrases that the user is Instructed to use.

Language Understanding (LUIS)
Model can be integrated using the speech SDK to recognize more complex intents with natural language.

SSMC Speech Synthesis Markup Language (SSML)
Adjusts the pitch, pronunciation, speaking rate, and volume of the synthesized speech output from the speech service.

Key phrase extraction
Is part of the Text Analytics API and extracts the main talking points from text.
Key phrase extraction is not integrated with the Speech SDK.

Visemes
Used to lip-sync animations of faces to speech.
Visemes use the position of the lips, jaw, and tongue when making particular sounds.

Scoring profile
Are part of the index definition and boost the relevance search based on the fields that you specify.
To favorize new entries, you can use the date the product was added to boost its relevance score in search.

Computer Vision
Analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.
The endpoint used in crrl:
<Zone where Coputer Vison resource was created>.api.cognitive.microsoft.com....



MOST POPULAR

List of Classical Ballets

Art

Improve attention

Non-Shedding Dog

Visual Studio code in Azure ML and Git

Study notes

VS code is a great tool to create and maintain applications, but it is as well a great too to manage Azure cloud resources fully integrated with GitHub.
It has a massive collection of extensions.
Last but very important: you can manage Kubernetes cluster from Docker and Azure. That the cherry on top so far because any deep learning experiment can be developed, tested and debugged locally.

PowerShell commands history
C:UsersUSER_NAMEAppDataRoamingMicrosoftWindowsPowerShellPSReadline

How to use it in Azure Machine learning - experiments (Data science)?

Requirements:
Install:
  • Visual studio code
  • Visual studio code extensions - mandatory:
    • Python
    • Jupiter
    • Azure Machine Learning
  • Visual studio code extensions - useful:
    • Polyglot Notebooks - https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode
    • Remote SSH

Note before reading further.
If you have a fresh new install of VS Code, only on python kernel available, installed through VS Code then all may work somehow, I mean all except torch and Azure SDK
Otherwise:
Install Anaconda
Add all packages you need via conda (Anaconda navigator is really nice; need a good computer)
In VS Code select Python inconda environment
You will have all you ever need for any experiment and no problem with dependecies and pacjkages update.
In images (Docker or ACI) just keep simple; one pyton version and only packages you need via YAML file.


Upgrade pip (pip is a packages utility for python.
# In terminal run
python -m pip install -U pip

If you have not installed
# run in terminal or command windows prompt (as administrator)
# replace 19.1.1 with the last stable version, check https://pypi.org/project/pip/

python -m pip install downloads/pip-19.1.1-py2.py3-none-any.whl

#or
python -m pip install downloads/pip-19.1.1.tar.gz

#or
https://files.pythonhosted.org/packages/cb/28/91f26bd088ce8e22169032100d4260614fc3da435025ff389ef1d396a433/pip-20.2.4-py2.py3-none-any.whl -O ~/pip20.2.4 then do python -m pip install ~/pip20.2.4


Install basic python libraries used in Data Science (Azure ML)
pip install pandas
pip install numpy
pip install matplotlib
pip install scipy
pip install scikit-learn

Work with deep learning:
pip install torchvision
# if not, you may try
pip3 install torchvision

Just in case there are problems try this:
pip install --upgrade setuptools

Install python SDK
pip install azureml-core
pip install azure-ai-ml

Check:
pip list


Create first jupyter note.
Create a new file and give extension .ipynb
or
Use Command Palette (Ctrl+Shift+P) and run.
Create: New Jupyter Notebook

Write a test (python code) and run it.



To get data from Git or whatever other place you need wget utility.
In Linux you already have it.

On Windows and Mac OS:
Download it from https://www.gnu.org/software/wget/
Copy it (wget.exe) where you need ( do not leave it in downloads folder)
For example C:Wget or C:Program FilesWget (you must create Wget folder.

Add C:Wget in the Environment variable.
There are plenty of tutorials on the net, here there are two:
Windows - Command prompt: Windows CMD: PATH Variable - Add To PATH - Echo PATH - ShellHacks
Windows UI:How to Add to Windows PATH Environment Variable (helpdeskgeek.com)

Git / GitHub

Gits is part of anyone who run experiments.
Now VS Code fully integrate Git in UI

Install Git extension.

Basic operation:

1. Stage changes -> Click on + (right to added/changed/deleted file or on top line (Changes)
Write name of "Commit." and click "Commit"

2. Create Branch - 3 dots (very top)
Example "My New Branch"

3. Merge a branch to master:
- Click on 3 dots (very top)
- Select [Checkout to] and then the branch TO - master in this case
- Again 3 dots
- Click on [Branch] ->[Merge Branch] and then branch FROM - My New Branch, in this case
- Again 3 dots
- Select [Pull, Push] -> [Push]


Resources:
Working with Jupyter Notebooks in Visual Studio Code
Doing Data Science in Visual Studio Code
torch.nn — PyTorch 1.13 documentation




Happiness hormones

Dopamine- the hormone which functions as a neurotransmitter (chemical released by nerve cells to send signals to other nerve cells) and plays a major role in the motivational component of reward-motivated behavior. Dopamine increases enjoyment and is necessary for changing bad habits.
Dopamine is the reward hormone and is produced while eating, goal achieving, solving problems and taking care of yourself.

Endorphins - the hormone which inhibits pain signals and produce euphoria, similar to that produced by other opioids. Endorphins provide pain relief and feeling of elation.
Endorphins is smoothing the pain and is produced by physical training, music listening, watching movies and laughing.

Oxytocin - the hormone responsible with attachment (mother-child attachment, lovers attachment, social bonding). Oxytocin promotes feeling of trust, love and connection and reduces anxiety.
Oxytocin is the love hormone and is produced when socializing, human contact, animals caressing, helping people.

Serotonin is responsible for well being (positive thinking and chronic pain regulator at brian level) and is produced by sun exposure, meditation, being close to the nature and mental health. Serotonin improves willpower, motivation and mood.

Norepinephrine enhances thinking, focus and dealing with stress.
Norepinephrine is the hormone of positive thinking (increases attention to positive events and decreases attention to negative thinking). Also, it has important role in chronic pain processing. Exercise, a good night's sleep and even getting a massage can increase the levels of Norepinephrine.

Melatonin enhances the quality of sleep.
Go out in sunlight to improve the release of melatonin which will halp you to get a batter night's sleep.

Endocannabinoids improve your appetite and increase feelings of peacefulness and well-being.

GABA (Gamma-aminobutyric acid) increases feelings of relaxation and reduce anxyety.

The brain does not distinguish between imagination and reality.
Life experiences will determine if a gene responsible with a specific mood will express itself or not. For example the sadness gene which is written on the 17th chromosome, will express itself only in case of traumatic events. If the individual experiences happiness throughout his life, the sadness gene will not express itself. In other words, our individual will not be a sad person without a reason, just because of his genetics.

The most common stress hormones are cortisol and adrenaline.


References:




The Upward Spiral, Using Neuroscience to Reverse the Course of Depression, One Small Change at a Time, By: Alex Korb

12 Most Common Competencies for Job Positions

When you deciding on which competencies are the most appropriate for you to learn in your chosen career field, you need to make the following considerations:
  • What will be the decision making or authority of the job position intended to occupy?
  • How much internal collaboration and interaction will be required?
  • How much contact and interaction with customers will be required to do?
  • What level of physical skills and knowledge will require this job?
Basic jobs consist of routine, clerical and manual work, which requires physical or on-the-job training.
Jobs up will require more responsibility and thus their level of authority will also increase.
Different competencies will be required to adjust to the demands of the job.

Bellow is a list of 12 competencies that are commonly found across many job positions and career fields:
  1. Time management and priority setting. Everybody. Time management describes the ability to manage and effectively use your time and other people's time. Candidates who have good time management are self-disciplined and can manage distractions while performing tasks. They are able to meet deadlines and communicate schedules effectively with teammates.

  2. Goal setting. Managerial or supervisory positions. They need to:
    - know how to plan activities and projects to meet the team or organization's predetermined goals successfully.
    - to understand how to establish goals with others
    - collaborate on a way forward.
    This will help them to elicit compliance and commitment from their team members or staff and thus make the journey toward the goal more efficient.

  3. Planning and scheduling work. Managerial positions or those working in production. This competency examines how well the candidate can manage and control workforce assignments and processes by utilizing people and process management techniques. It includes:
    - analyzing complex tasks
    - breaking complex task down to manageable units or processes, using the most effective systems to plan and schedule work
    - setting checkpoints or quality control measures to monitor progress.

  4. Listening and organization. Dealing with people and working in teams within the organization (collaboration or communicating with customers). It assesses the candidate's ability to understand, analyze and organize what they hear and respond to the massage effectively. Strengthening this competency will require:
    - practice identifying inferences and assumptions
    - reading body language
    - withholding judgments that could lead to bias
    - empathizing with others

  5. Clarity of communication. Managerial or supervisory positions. Whether the information is written or verbally communicated, it need to have:
    - a clear and concise way of delivering
    - the message have to reminding teams or staff members of objectives.
    The message would need to effectively overcome semantic or psychological barriers that may occur during interactions and maintain mutual understanding and trust.

  6. Obtaining objective information. Management. It encourages decision making and conflict resolution that is fair. Fairness is reached through various techniques:
    - asking probing questions
    - interviewing staff to obtain unbiased information
    - using reflective questions appropriately.
    Requires self-awareness and understanding of one's own biases and personal judgement.
    The outcomes are based on the evidence of facts instead of one's own beliefs about what is wright or wrong.

  7. Training, mentoring and delegating. Management roles.
    Training, mentoring and delegating help leaders, managers and supervisors understand their teams or staff.
    It makes leaders influential among their subordinates.
    Influence helps to direct the team towards the desired company or project goals.
    Influence helps leaders train and develop the people under them to perform at a higher level of excellence.
    The necessary skills required to train and influence a team or group successfully include:
    - coaching
    - advising
    - transferring knowledge and skills
    - teaching
    - giving constructive feedback and criticism.

  8. Evaluating employee performance. This competency describes the ability to:
    - design
    - test
    - undertake a team or individual performance evaluation by assessing past performance and agreeing on future performance expectations.
    Employees with this competency are skilled at:
    - developing evaluation parameters
    - benchmarking performance
    - evaluating face to face confrontation with staff without holding any bias.

  9. Advising and disciplining. Managerial or supervisory positions. They will need to know how to advise and counsel employees and fairly undertake disciplinary measurements. The goal of disciplining is to restore the optimum performance of subordinates while maintaining respect and trust. Deviations from company policies, standards and culture can cost the organization a lot of money and time. Therefore, managers will need to know how to impose penalties, warnings and sanctions with firmness in appropriate circumstances.

  10. Identifying problems and finding solutions. All employees. Problem solving involves:
    - identifying the internal and external barriers with prevent achievement of a particular goal or standard
    - applying systematic procedures to reduce or eliminate problems during the implementation of strategies and actions.
    Effective problem solving involves:
    - investigating symptoms
    - distinguishing between various problems
    - assessing inputs and outcomes
    - assessing evidence related to the problem
    - planning and recommending relevant interventions.

  11. Risk assessment and decision making. Managerial or supervisory positions.
    The type of decision making required involves committing to company resources and processes that carry company wide implications.
    The problem solving competency requirements, assessing risk and making decisions require appropriate interventions and alternatives to be identified. Every intervention must be weighted for its strengths and weakness and the level of risk associated. After, the best option to achieve the desired goal is selected.

  12. Thinking analytically. Managerial or supervisory positions. It involves skills such as:
    - assessing information
    - reaching logical conclusions
    - separating facts from opinions
    - staying clear from unwanted assumptions
    - making decisions primarily based on valid premises and sufficient information.
    Analytical thinking helps leaders plan for future interventions and appropriately organize company resources.


Resources:
The untold secrets of the job search, Book by Zane Lawson

Happiness and rational thinking

Happiness is post cognitive: If you think correctly and rational, than your emotions are functional and healthy.

From scientific point of view the happiness is generated: 50% are genetic factors (the difference between two people happiness is due genetic inheritance and not to be happy or not), 40% are psycho-social factors (our way of thinking) and 10% are your life experience factors.

Types of happiness:

1) Pleasant life (hedonistic happiness, happiness as emotions) - well-being with very strong physiological load. This happiness happens when you are doing what you love: if you love to read, you read, if you like to travel, you travel etc. to obtain the happiness. You obtain happiness by planning to do what you like every week (hobbies).

2) Good life - to acknowledge your strong points (we are very good in doing something but we focus in doing something else). It is very important to involve yourself in activities which will use your strong points. In this way you get maximum of productivity minimizing your efforts. Good life is guaranteed because does not exist discrepancy between what you expect and what you are doing.
Ideal ego and real ego. The human mind is not liable to rationality, he is liable to irrational. We are not able to find the true without assistance and we have the tendency to mistake our strong points with something ideal (positive illusions). To find our real ego we need guidance (the school to identify children’s talents).

3) Life with meaning (eudemonic happiness) - to have very clear values and your life to be in concordance with these values. 3 to 5 main values. If your life is subordinated to your values, no matter what will happen, you are protected. Maybe you are not the happiest (active emotional and with a physiologic raiser rate very high), but you will be satisfied with your life.

Styles of thinking:

1) Physiological, rational thinking is:
- having flexible mind - your goals and desires are formulated in preferential terms (ai wish , I do everything to , but you are ready to accept that what you want could be not happening)
- non catastrophic thinking
- thinking with frustration tolerance
- thinking with nuanced evaluation

2) Irrational thinkingis:
- rigid thinking in terms of “must”
- catastrophic thinking
- no tolerance to frustration
- thinking with global evaluation
In case of an unfortunate event, people with irrational thinking are falling in depression, people thinking with reason are sad individuals but their sadness is a sign of being normal and is concurrent with happiness. The way people are thinking makes the difference between a healthy person and a person with psychological problems. The healthy people can experience the happiness.

The human mind is not liable to rationality, he is liable to irrational. The rationale thinking is something that we need to learn, to educate ourselves.

Negative emotions vs. pathological emotions:
- sadness vs. depression
- dissatisfied vs. angry and aggressive
- worries vs. panic and anxiety
Positive emotions with negative outcome:
- authority with tyrannical effect

Pre-purpose and post-purpose emotions:

Pre-purpose emotions = emotions which motivates for the task (optimism, hope) - it activates the working memory
Post-propose emotions = satisfaction emotions - it helps to sediment the experiences into long term memory

Pre-purpose and post-purpose emotions have to have always this order and never to be skipped one of them. Pre-purpose emotions will prepare you for the task ant post-purpose emotions will enable you to be happy about what you did.

Positive thinking theory:

The theory of “positive thinking “ I could be a wrong concept; it should be promoted rational thinking with positive or negative content. As a person you have value through your existence and not through what you are doing. You exist and only your behavior can be positive or negative and not you as a person.
Positive thinking is common sense, but positive thinking in unfortunate events may create unwanted emotions by running into irrational thinking (must be). No backup if things are not happening as planned. Rational thinking (flexible thinking) creates the healthy emotions. They may be negative emotions, but not pathological.

The positive illusions and optimism are good on a realistic background. Positive illusions and optimism should be based on rational thinking always (backup if that ideal situation does not occur).



References:

Purebred Dog Breeds

Working Dogs

Hound Dogs

Herding Dogs

Companion Dogs

Lab hands-on Pandas and Matplotlib

Study notes

Load and clean data

# Load data from csv file
!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv
df_students = pd.read_csv('grades.csv',delimiter=',',header='infer')

# Remove any rows with missing data
df_students = df_students.dropna(axis=0, how='any')

# Calculate who passed.
# Assuming 60 is the grade, all students with Grade >=60 get True, others False
passes = pd.Series(df_students['Grade'] >= 60)

# Add a new column Pass that contains passed value per every student, see above
df_students = pd.concat([df_students, passes.rename("Pass")], axis=1)

# dataframe
df_students

Result:

Name
Name
#NameStudyHoursGradePass
0Dan10.0050.0False
1Joann11.5050.0False
2Pedro9.0047.0False
3Rosie16.0097.0True
4Ethan9.2549.0False

Visualise data with matplotlib
# Plots shown inline here
%matplotlib inline

from matplotlib import pyplot as plt

# set sizes for the box graph (no this - graph will be created as a square)
plt.figure(figsize=(12,5))

#create bar chart
plt.bar(x=df_students.Name, height=df_students.Grade)

# Title
plt.title('Students Grades')
plt.xlabel('Student')
plt.ylabel('Grade')
# Show y labels on vertical
plt.xticks(rotation=90)
# Show grid
plt.grid(color="#cccccc, linestyle='--', linewidth=2, axis='y', alpha=0.7)

#display it
plt.show()

# Compute how many students pass and how many fails
rez =df_students.Pass.value_counts()
print(rez)

Result:
False 15
True 7
Name: Pass, dtype: int64
Keys (index) False and True
They will be used in legend for pie chart bellow.

Figure with two subplots
%matplotlib inline
fig, ax = plt.subplots(1,2, figsize=(10,4))

# Create bar chart plot
ax[0].bar(x=df_students.Name, height=df_students.Grade, color='green')
ax[0].set_title('Grades')
ax[0].set_xticklabels(df_students.Name, rotation=90)

# Create pie chart plot
pass_count = df_students['Pass'].value_counts()

# Above can be pass_count=df_students.Pass.value_counts() caount haw many Pass and Not Pass are
ax[1].pie(pass_count, labels=pass_count)
ax[1].set_title('Passing Count')
# Build a list where label name is the key from pass_count dataset and the explanation si the value.
ax[1].legend(pass_count.keys().tolist())

#Ad subtitle to figure (with 2 subplots)
fig.suptitle('Student Data')

#Show
fig.show()



Pandas includes graphics capabilities.

# Automatic lables on y rotation, automatic legend generation
df_students.plot.bar(x='Name', y='StudyHours', color ='green', figsize=(5,2))



Descriptive statistics and data distribution
Read this first.
Grouped frequency distributions (cristinabrata.com)

Q: How are Grades values distributed across the dataset (sample), not dataframe? Data distribution study unidimensional array in this case.
A: Create a histogram.

%matplotlib inline

from matplotlib import pyplot as plt

# Create data set
var_data = df_students.Grade

# Create and set figure size
fig = plt.figure(figsize=(5,2))

# Plot histogram
plt.hist(var_data)

# Text
plt.title('Data distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')

fig.show()

Looking to understand how values ae distributed, measure somehow to find the measure of central tendency (midle of distributin / data)
  • mean(simple average)
  • median(value in the middle)
  • mode(most common occurring value)

%matplotlib inline

from matplotlib import pyplot as plt

# Var to examine
var = df_students['Grade']

# Statistics
min_val = var.min()
max_val = var.max()
mean_val = var.mean()
med_val = var.median()
mod_val = var.mode()[0]

print('Minimum:{:.2f}Mean:{:.2f}Median:{:.2f}Mode:{:.2f}Maximum:{:.2f}'.format(min_val, mean_val, med_val, mod_val, max_val))

# Set figure
fig = plt.figure(figsize=(5,2))

# Add lines
plt.axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth=2)
plt.axvline(x=max_val, color = 'cyan', linestyle='dashed', linewidth=2)
plt.axvline(x=med_val, color = 'red', linestyle='dashed', linewidth=2)
plt.axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth=2)
plt.axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth=2)

# Text
# Add titles and labels
plt.title('Data Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')

# Show
fig.show()

Result:
Minimum:3.00
Mean:49.18
Median:49.50
Mode:50.00
Maximum:97.00


Two quartiles of the data reside: ~ 36 and 63, ie. 0-36 and 63-100
Grades are between 36 and 63.

As a summary: distribution and plot box in the same figure

Another way to visualize the distribution of a variable is to use a box plot (box-and-whiskers plot)
var = df_students['Grade']
fig = plt.figure(figsize=(5,2))
plt.boxplot(var)
plt.title('Data distribution')

fig.show()

It is diferent from Histogram.
Show that 50% of dataresides - in 2 quartiles (between 36% abn 63%), the other 50% of data are between 0 - 36% and 63% -10%

Most common approach to have at a glance all is to build Histogram and Boxplot in the same figure.
# Create a function show_distribution
def show_distribution(var_data):
from matplotlib import pyplot as plt
min_val = var_data.min()
max_val = var_data.max()
mean_val = var_data.mean()
med_val = var_data.median()
mod_val = var_data.mode()[0]

fig, ax = plt.subplots(2, 1, figsize = (5,3))
# Plot histogram
ax[0].hist(var_data)
ax[0].set_ylabel('Frequency')

#Draw vertical lines
ax[0].axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)
ax[0].axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)
ax[0].axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)
ax[0].axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)
ax[0].axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)

#Plot the boxplot
ax[1].boxplot(var_data, vert=False)
ax[1].set_xlabel('Value')
fig.suptitle('Data Distribution')

fig.show()

col = df_students['Grade']
#Call function
show_distribution(col)

Result:
Minimum:3.00
Mean:49.18
Median:49.50
Mode:50.00
Maximum:97.00


Central tendencyare right in the middle of the data distribution, which is symmetric with values becoming progressively lower in both directions from the middle

The Probability Density Functionis well implemented in pyplot

# Make sure you have scipy.
# How to install
# pip install scipy (run this in CS Code terminal

def show_density(var_data):
from matplotlib import pyplot as plt

fig = plt.figure(figsize=(10,4))

# Plot density
var_data.plot.density()

# Add titles and labels
plt.title('Data Density')

# Show the mean, median, and mode
plt.axvline(x=var_data.mean(), color = 'cyan', linestyle='dashed', linewidth = 2)
plt.axvline(x=var_data.median(), color = 'red', linestyle='dashed', linewidth = 2)
plt.axvline(x=var_data.mode()[0], color = 'yellow', linestyle='dashed', linewidth = 2)

# Show the figure
plt.show()

# Get the density of Grade
col = df_students['Grade']
show_density(col)


The density shows the characteristic "bell curve" of what statisticians call a normal distribution with the mean and mode at the center and symmetric tails.


References:
Exam DP-100: Designing and Implementing a Data Science Solution on Azure - Certifications | Microsoft Learn
Welcome to Python.org
pandas - Python Data Analysis Library (pydata.org)
Matplotlib — Visualization with Python0

Toy Dog Breeds

Improve memory

Visual Studio code - Azure CLI basic

Study notes

Why Azure CLI
To train a model with Azure Machine Learning workspace, you can use:
  1. Designer in the Azure Machine Learning Studio
  2. Python SDK
  3. Azure CLI. To automate the training and retraining of models more effectively, the CLI is the preferred approach.
Open VS code and then a Power Shell. Run:
az --version
Result:
azure-cli 2.45.0
....
Extensions:
ml 2.14.0

You must have version 2.x for both.

if not run:
az upgrade
az extension remove -n azure-cli-ml
az extension remove -n ml
az extension add -n ml -y

Assume you are logged in (if not az login)
Check/ set active subscription.
az account show
# get the current default subscription using show
az account show --output table
# get the current default subscription using list
az account list --query "[?isDefault]"
# change the active subscription using the subscription ID
az account set --s "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"

Create resource group
# It will be created in active subscription
az group create --name "GROUP_NAME"--location "eastus"
# Set it default
az configure --defaults group="GROUP_NAME"

Create workspace
# It will be created in active scubscription and default resource grop
az ml workspace create --name "WS_NAME"
#Set the default workspace
az configure --defaults workspace="WS_NAME"

Create compute instance
# It will be created in active subscription and default resourcegroup and default workspace
#--resource-group: Name of resource group. If you configured a default group with az configure --defaults group=<name>, you don't need to use this parameter.
#--workspace-name: Name of the Azure Machine Learning workspace. If you configured a default workspace with az configure --defaults workspace=<name>, you don't need to use this parameter.
#--name: Name of compute target. The name should be fewer than 24 characters and unique within an Azure region.
#--size: VM size to use for the compute instance. Learn more about supported VM series and sizes.
#--type: Type of compute target. To create a compute instance, use ComputeInstance
az ml compute create --name "INSTANCE_NAME" --size STANDARD_DS11_V2 --type ComputeInstance

Create compute cluster
# It will be created in active subscription and default resourcegroup and default workspace
#--type: To create a compute cluster, use AmlCompute.
#--min-instances: The minimum number of nodes used on the cluster. The default is 0 nodes.
#--max-instances: The maximum number of nodes. The default is 4.
az ml compute create --name "CLUSTER_NAME" --size STANDARD_DS11_V2 --max-instances 2 --type AmlCompute

Create dataset
Necessary two files:
data_local_path.yaml
$schema: https://azuremlschemas.azureedge.net/latest/data.schema.json
name: lab-data
version: 1
path: data
description: Dataset pointing to diabetes data stored as CSV on local computer. Data is uploaded to default datastore.
lab.data.csv

Run:
az ml data create -- file ./PATH_TO_YAML_FILE/data_local_path.yaml

When you create a dataset from a local path, the workspace will automatically upload the dataset to the default datastore. In this case, it will be uploaded to the storage account which was created when you created the workspace.
Once the dataset is created, a summary is shown in the prompt. You can also view the environment in the Azure ML Studio in the Environments tab.

List datastores
az ml datastore list

Find it in Azure UI
Storage (Under resource where is the Azure ML workspace)
  • Storage browser
  • Blob containers
  • azureml-blobstore.....
  • LocalUpload
Here are all about Azure ML including data related with the environment.

Create environment.
You expect to use a compute cluster in the future to retrain the model whenever needed. To train the model on either a compute instance or compute cluster, all necessary packages need to be installed on the compute to run the code. Instead of manually installing these packages every time you use a new compute, you can list them in an environment.
Every Azure Machine Learning workspace will by default have a list of curated environments when you create the workspace. Curated environments include common machine learning packages to train a model.
Necessary two files (in the same folder for this example):

basic-env-ml.yml
name: basic-env-ml
channels:
- conda-forge
dependencies:
- python=3.8
- pip
- pip:
- numpy
- pandas
- scikit-learn
- matplotlib
- azureml-mlflow

basic-env.yml

$schema: https://azuremlschemas.azureedge.net/latest/environment.schema.json
name: basic-env-scikit
version: 1
image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
conda_file: file:conda-envs/basic-env-ml.yml

Run
az ml environment create --file ./PATH_TO_YAML_FILE/basic-env.yml

Stop instance:
az ml compute stop --name "INSTANCE_NAME"

List resources groups
az group list --output table

Delete resource group
az group delete --name GROUP_NAME

Delete workspace
az ml workspace delete



References:
How to manage Azure resource groups – Azure CLI | Microsoft Learn
Manage workspace assets with CLI (v2) - Training | Microsoft Learn

Terrier Dogs

Cognitive Schema questionnaire

How to use this questionnaire:

In this questionnaire you will find affirmations that you will use them to describe yourself. Read each affirmation and choose the best answer that define you based on last year. Choose your answer based on your emotional feelings and not based on logic and what you think is correct answer.

1. Read carefully the How to use this questionnaire section.
2. Choose for each affirmation one of the answers below, between 1 to 6, which one fits you best.
3. Write down the question number with the corresponding answers 1 to 6.
4. Compute the total by summarize all the answers.
5. Compare your score with the corresponding threshold. The score over the threshold shows how serious the problem is.
6. Acknowledging the problem you can find how to fix it. Professional help may need it.

Possible answers:

The below questionnaire contains statements used by a person to describe her/himself. Please read each statement carefully and decide how well it describes you. When you are not sure, answer according to what you feel, and not according to what you think is true.

Each statement of these questionnaire has 6 possible answers from 1 to 6.
The answers are (every time in same order for each affirmation):

1. Completely untrue to me
2. Partly untrue to me
3. More true than untrue
4. Mainly true
5. Mostly true about me
6. Describes me perfectly



Questionnaires

Emotional Deprivation (ED)

- the others don’t offer us the nurturance, empathy and protection we need.

1. Usually I didn't have someone to take care of me, to share my life with, or to care much about what was happening to me.

25. Usually, people were not by my side to show me warmth, support and love.

49. For a long time in my life I did not feel that I was special to someone.

73. Most of the time I didn't have someone who really listened to me, who understood me or who invested emotionally in me.

96. Rarely I have had a person to advise me or guide me when I didn't know what to do.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Emotional deprivation ED score:
ED threshold: 7on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Abandonment / Instability (AB)

- the feeling that the close ones will not be able to emotionally support us, or to protect us and they will abandon us in favor of someone better.

2. I clung to those close to me because I was afraid they would leave me.

26. I need the others so much that I'm worried I'll lose them.

50. I am afraid that the people I feel close to will leave me or abandon me.

74. I despair when I feel that someone I care about is moving away from me.

97. Sometimes I am so worried that others will leave me that I send them away from me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Abandonment / instability AB score:
AB threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Mistrust / Abuse (MA)

- the belief that in the end, the others will intentionally hurt, abuse, humiliate, cheat, lie, manipulate, or take advantage on us.

3. I feel that people will take advantage of me.

27. I feel that I must not let my guard down in the presence of others, because otherwise they will intentionally hurt me.

51. It's just a matter of time before someone betrays me.

75. I'm pretty suspicious of others motivations.

98. I am usually vigilant about others motivations.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Mistrust/ abuse MA score:
MA threshold: 11 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective.The score over the threshold shows how serious the problem is.

Social isolation / Alienation(SI)

-the sense that someone is different from others and is not part of any group.

4. I can't find my place anywhere.

28. I am fundamentally different from the others.

52. My place is not here; I'm lonely.

76. I feel alienated from other people.

99. I always feel out of the group.

Calculate your score:
SCORING KEY
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Isolation / alienation SI score:
SI threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.


Defectiveness / Shame (DS)

- the feeling that one is bad, unwanted, inferior, in important respects; or that one would be unlovable to significant others.

5. No man / woman I know can love me once he / she sees my flaws.

29. None of those I want to be with will be able to stay next to me once they get to really know me.

53. I do not deserve the love, attention and respect of others.

77. I feel like I can't be loved.

100. I have too many unacceptable flaws in many areas to let others know me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Defectiveness / shame DS score:
DS threshold: 7 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Failure (FA)

- the belief that one has failed and will inevitably fail in areas of achievement, so he is stupid, lower in status, or less successful than others.

9. In most school or work activities I'm not as good as the others.

33. I am incompetent when it comes to achievements.

57. Most others are more capable than me in terms of professional and achievements.

81. I'm not as talented at work as the others.

103. I'm not as smart as others when it comes to school or work.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Failure FA score:
FA threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Dependence / Incompetence (DI)

- belief that one needs considerable help from others to handle one’s everyday responsibilities in a competent manner.

10. I don't feel able to manage on my own in my daily life.

34. I see myself as a dependent person when it comes to everyday life.

58. I am less capable than most people.

82. My judgment cannot be good in all everyday situations.

104. I do not trust my ability to solve all the problems that arise.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Dependence / incompetence DI score:
DI threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Vulnerability to harm or illness (VH)

- exaggerated fear that imminent illnesses, emotional or external catastrophe will strike at any time and that one will be unable to prevent it.

11. I can't get rid of the feeling that something bad is about to happen.

35. I feel that a disaster (natural, chemical, medical or criminal) can happen at any time.

59. I'm afraid I'll be attacked.

83. I'm afraid I'm going to lose all my money and get poor.

105. I'm afraid I have a serious illness, even though the doctor didn't diagnose anything serious for me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Vulnerability to harm or illness VH score:
VH threshold: 6 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Enmeshment / Undeveloped Self (EM)

- excessive emotional involvement and closeness with one or more significant others (often parents), at the expense of independence and normal social development.

12. I have not been able to separate (walk away) from my parents in the way other people of my age do.

36. My parents and I tend to get involved in each other's lives and problems.

60. It is very difficult for me and my parents to keep intimate secrets from each other without feeling guilty and deceived.

84. I often feel that if my parents live through me, I no longer have a life of my own.

106. I often feel that I do not have a separate identity from my parents or my partner.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Enmeshment / undeveloped self EM score:
EM threshold: 9 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Subjugation (SB)

- suppression of one’s preferences, decisions, desires and suppression of emotional expression, especially anger usually to avoid the abandonment.

13. I think if I do what I feel, I get just in trouble.

37. I feel that I have no choice but to fulfill the wishes of others, otherwise they will reject me.

61. In relationships, I let the other have the last word.

85. I always let others decide for me, so I don't know what I want for myself.

107. It is very difficult for me to ask that to be respected my rights and that to be taken into account my feelings.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Subjugation SB score:
SB threshold: 8 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Self-sacrifice (SS)

- excessive focus on voluntarily meeting the needs of others in daily situations, at the expense of one’s own gratification.

17. I'm the kind of person who usually ends up taking care of those close to me.

41. I am a good person because I think of others more than myself.

65. I am so preoccupied with dealing with the people I care about that I have little time for myself.

89. I've always been the kind of person who listens to the problems of others.

110. Other people know me as doing too much for others and not doing enough for me.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Self sacrifice SS score:
SS threshold: 18 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.


Emotional inhibition (EI)

- inhibition of anger, inhibition of positive impulses, difficulty expressing vulnerability or communicating freely about one’s feelings, needs and excessive emphasis on rationality while disregarding emotions.

18. I try too hard to express my positive feelings towards others (affection, concern).

42. I find embarrassing to express my feelings in front of others.

66. I find it hard to show warmth and spontaneity.

90. I control myself so much that people think I have no emotions.

111. People see me as emotionally inflexible.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Emotional inhibition EI score:
EI threshold: 10 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Unrelenting Standards / Hyper-criticality (US)

- the belief that one must strive to meet very high internalized standards, usually to avoid criticism. Its forms are the perfectionism, the excessive attention to details, the rigid rules and the “should”.

19. I have to be the best in everything I do: I don't accept being in second place.

43. I always try to do everything I can / everything that depends on me; I'm not satisfied with "almost good".

67. I have to fulfill all my responsibilities.

91. I feel that there is a constant pressure on me to fulfill and achieve different things.

112. I can't apologize for my mistakes.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Unrelenting standards / hyper-criticality US score:
US threshold: 17 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Entitlement / Grandiosity (ET)

- the belief that one is superior to other people, that claim the right to do or have whatever want, regardless of what is realistic, or the cost to others, all this in order to get control and power.

20. I have problems when I have to accept "no" in response, when I want something from others.

44. I am special and I do not have to accept restrictions imposed by others.

68. I hate being constrained or restrained from what I want to do.

92. I feel that I do not have to follow the rules, norms and conventions that others make.

113. I feel that what I have to offer is more valuable than the contributions of others.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Entitlement / grandiosity ET score:
ET threshold: 14 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Insufficient Self-Control / Self-Discipline (IS)

- the difficulty to practice self-control and discipline to achieve one’s personal goals, or to restrain the excessive expression of one’s emotions and impulses, the excessive desire to maintain the comfort and to avoid unpleasant situations.

21. I can't motivate myself to perform boring and routine tasks.

45. If I can't reach a goal, I quickly become frustrated and give up.

69. I do not sacrifice immediate satisfaction to achieve a distant goal.

93. I can't force myself to do things I don't like, even if I know it's for my own good.

114. I was rarely able to rely on my own decisions.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Insufficient self-control / self-discipline IS score:
IS threshold: 12 on a scale from 0 to 30.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Approval-Seeking / Recognition-Seeking (AS)

- excessive emphasis on gaining approval, recognition, attention from other people, the one’s sense of esteem is dependent on the reactions of others.

6. It is important for me to be liked by almost everyone I know.

14. I change depending on the people I am with, so that they like me more.

22. I'm trying to adapt.

30. My self-esteem is mostly based on how others see me.

38. Having money and knowing a lot of "good" people makes me more valuable.

46. I invest a lot of time in the way I look, so that everyone around me can appreciate me.

54. My own achievements are more valuable to me if people notice them.

62. I'm so preoccupied with getting used to it that sometimes I forget who I am.

70. I find it difficult to set my own goals without thinking about how others will react to my choices.

78. When I think about the decisions in my life, I realize that I made most of them with the approval of others.

86. Even if I don't like someone, I still want him / her to like me.

94. When I don't get much attention from others, I feel unimportant.

101. I seek recognition and admiration when I express my opinion at a meeting or gathering.

108. Numerous compliments and rewards make me feel valuable.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Approval-Seeking / Recognition-Seeking AS score:
AS threshold: 35 on a scale from 0 to 84.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Negativity / Pessimism (NP)

– an excessive focus on the negative aspects of life and minimizing or neglecting the positive aspects.

7. Even when things seem to be going well, I think it's only temporary.

15. If something good happens sometimes, I'm afraid something bad will happen.

23. You can't always be careful enough; something bad will always happen.

31. No matter how hard I work, I'm afraid I might run out of money.

39. I'm worried that a wrong decision can lead to disaster.

47. I am often obsessed with minor decisions because the consequences of a mistake can be serious.

55. I feel better pretending that things will not go well for me, so that I don't feel bad if things don't really go well.

63. I focus mainly on negative events and life situations.

71. I tend to be pessimistic.

79. People close to me think I'm too worried.

87. If people get excited about something, I feel uncomfortable and feel the need to warn them that something bad is going to happen.


Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Negativity / Pessimism NP score:
NP threshold: 21 on a scale from 0 to 66.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.

Punitiveness (PU)

– the belief that people should be punished for making mistakes.

8. If I make a mistake, I deserve to be punished.

16. If I don't do everything that depends on me, I can expect to lose.

24. There is no excuse if I'm wrong.

32. People who do not know their limits should be punished.

40. I generally do not accept the apologies of others. They are not willing to take responsibility and bear the consequences.

48. If I don't do my job, I should suffer the consequences.

56. I often think about the mistakes I make and I am angry with myself.

64. When people do something wrong, I have trouble applying the "forgive and forget" principle.

72. I can't forgive even if the person has apologized.

80. I get upset when I think someone gave up something too quickly.

88. I get annoyed when people apologize and blame others for their problems.

95. It doesn't matter why I'm wrong; when I did something wrong, I have to pay.

102. I blame myself for the things I failed at.

109. I am a bad person who deserves to be punished.

Calculate your score:
SCORING KEY
1 for Completely untrue to me
2 for Partly untrue to me
3 for More true than untrue
4 for Mainly true
5 for Mostly true about me
6 for Describes me perfectly

Choose for each affirmation the answer (1 to 6) which best fits you.

Interpreting your Punitiveness PU score:
PU threshold: 36 on a scale from 0 to 84.
The score up to threshold inclusive shows normality from psychological perspective. The score over the threshold shows how serious the problem is.


References:

Chestionar YSQ - 114 intrebari

Gemstones

Pearl

Pearl is an organic creation found in the sea, a natural phenomena that actually occurs as part of the defense mechanism of a mollusc shell. Coming in all shapes, sizes and colors, Pearl has shown itself to be remarkably versatile when it comes to jewelry design, and has a fascinating and rich history built up over thousands and thousands of years.


Perls are believed to have been revered and traded as long as 6,000 years ago. The first Pearls to be held by human hands were likely discovered on the shores of India as fish-eating locals searched for food. Pearls were being given as ornamental gifts in Ancient China as far back as 2,250 BC and have been used in jewelry as personal adornment since at least 450 BC.

With demand outstripping supply by such a large degree, Japanese entrepreneur Mikimoto Kokichi set out to discover a way to cultivate Pearls and bring an element of stability to their availability. In 1893 he made a revolutionary breakthrough when he and his wife Ume successfully cultured a Pearl under human supervision, and over the following years he perfected his technique, a combination of art and science. We are now able to culture Pearls in special oyster nurseries where these mystifying creatures are protected and tended to by expert guardians. Each mollusc is painstakingly cared for over a number of years, allowing nature to do what she always does best - create a stunning gemstone.

A Pearl is one of just a handful of organic gems (the other well-known ones being Coral, Amber and Jet). Rather than being a mineral, Pearls actually grow inside a mollusc, a term used for all shells that open and close on a hinge, such as oysters, clams, and mussels. Organic gemstones don’t usually have a traditional crystal structure or composition, but Pearl is made up of calcium carbonate (mostly Aragonite), which features an orthorhombic crystal structure. This builds the Pearl up in concentric layers, leading to the unique visual quality that Pearls display.
Pearl formation is fascinating, and is totally different to how other gemstones form. If a foreign object, such as a grain of sand, enters a mollusc it becomes an irritant to the creature inside, so in order to protect itself it releases a silky substance, known as nacre (essentially Mother-of-Pearl, which also lines the shell), to cover the uninvited guest. Over time the mollusc will continue to release nacre over the foreign body and when the mollusc is opened years after the initial intrusion, the uninvited guest has been turned into a glorious Pearl. Pearls essentially exist as the result of a defence mechanism. It is amazing to think how nature can turn an unwelcome grain of sand into one of the most gorgeous gems in the world.

The term 'Cultured Pearl' alone tends to mean that the Pearl has been cultivated in the ocean (saltwater) whereas 'Freshwater Cultured Pearl' refers to those that are cultivated inland in lakes. Over 99% of Pearls sold today worldwide are Cultured Pearls of one type or the other.
When buying new Pearls, it is not really a case of natural Pearls versus Cultured Pearls, but Cultured Pearls versus entirely synthetic, man-made Pearls. If you own Pearls and are not sure if they are genuine or not, a great way to test them is to rub them on your teeth. If the Pearl feels slightly grainy rather than smooth, it’s a real Pearl, be that natural or cultured. If it’s smooth, however, you know it is not a real Pearl, as companies who produce imitation, synthetic Pearls have yet to master the grainy effect of natural nacre. Quite often these imitations are just plastic or glass made to look like a Pearl.

Round, flawless, and orient are words you’ll hear relating to Pearls and these are qualities used to determine their value.
The word round seems a bit of an obvious one to describe a Pearl but it is in fact the most important. It’s a common mistake to think Pearls have been faceted in some way to give them their perfect spherical shape, when in fact the shape of a Pearl is all down to the work done by the mollusc. Because no two Pearls are identical in shape or size, it takes a skilled jeweler hours and hours to select matching Pearls when stringing them together for necklaces and bracelets.
The finest Pearls do not have any flaws, bumps or marks in the nacre and they should have an even and clean surface.
The final consideration when valuing Pearls is their orient. This is the word used to describe the luster of a Pearl (also referred to as pearlescence). The orient is a soft iridescence caused by the refraction of light off the layers of nacre.

Every Pearl is individually graded on five virtues, the first of which is its luster. The luster of a Pearl is the most important factor but also the hardest to determine. However, with a trained eye it becomes an instinctive process. A Pearl’s complexion is under significant scrutiny as any imperfections may alter their value.

Due to the Pearl's shape it has often been associated with the moon.

GEMOLOGICAL PROPERTIES OF PEARL

  • Color White, pink, yellow, orange, silver, cream, purple, golden, brown, green, blue, black
  • Family Organics
  • Mohs Scale Hardness 2.5 - 4.5
  • Specific Gravity 2.60 - 2.85
  • Refractive Index 1.52 - 1.69
  • Luster Pearly
  • Crystal System Orthorhombic
  • Transparency Translucent
  • Chemical Formula CaCO3
  • Composition Calcium Carbonate

Where do Pearls come from? Well, those known as Freshwater Cultured Pearls are created using freshwater river mussels and farmed from sources mostly in Japan and China. Cultured Pearls (without 'Freshwater' in the description) are created using saltwater Pearl oysters and are largely farmed from the oceans around the Philippines, Australia and French Polynesia. The variance in locale and the mollusc used to create this organic gem give us the different hues of Pearl.

PEARL VARIETIES

The Pinctada Maxima silver-lipped oyster is found in Australia and produces incredible white-silver Pearls. It is considered the largest and rarest of the Pearl oysters, and is valued highly not just because of the Pearls created, but also because of the sheer quality of its Mother-of-Pearl, which is sought after in its own right. The farm from where these Pearls are sourced is harboured in the perfect spot (chosen from a long history of trial and error), allowing the oyster to absorb the rich nutrients and minerals from the immaculate waters that surround it. These oysters are tended by hand, painstakingly cleaned and protected in the deep from their formidable predators and environment. The oyster’s gift is a perfect silver-white Pearl, each one a unique treasure from the deep. There is also a golden-lipped Pinctada Maxima oyster which provides a sublime golden variety of South Sea Pearl. Pearls from these waters can very rarely display other subtle variations in hue.

In the atolls and lagoons of French Polynesia exists a unique paradise that brings the world the Tahitian Pearl. This Pearl is a relative newcomer to the market, having been popularized in the later 19th century. It is a natural, dark, exotic Pearl sometimes known as a Black Pearl, though they actually come in every color other than jet black. Its myriad of different colors make strands of striking Pearls that are almost impossible to produce and extremely difficult to match. Behind every great Pearl is its oyster. The Mother-of-Pearl that brings us this Tahitian variety is the illustrious and legendary Pinctada Margaritifera or black-lipped Pearl oyster. Rarely has one of the most expensive Pearls on the planet run the risk of being upstaged by its host, but this is a testament to this beautiful and mysterious oyster.

Maruata Pearls are sourced in the crystal clear waters of French Polynesia, like their Tahitian counterparts mentioned above. The Maruata Pearl nursery has a world-class reputation for producing some of the very finest Black Pearls on the market, and the farmers care for and nurture the Pinctada Margaritifera oysters on a daily basis. The oysters are regularly cleaned and moved into new areas of perfect temperature and suitability should the ideal cultivation conditions fluctuate. The reason we differentiate between Tahitian and Maruata Pearls at Gemporia, even though they share a provenance, is because Maruata Pearls are afforded extra time to grow - normally 21 months to two years where other Pearls may be given 12 to 18 months. To signify their longer gestation period and resultant larger size, we only set Maruata Pearls into gold when making our Pearl jewelry.

Some of the world’s most beautiful Freshwater Cultured Pearls come from the Kaori Pearl nursery, one of the biggest Pearl farms in China, where they take a considerable amount of time to cultivate their finished Pearls. Their reputation within the industry is stellar. The freshwater lakes of China offer ideal growing conditions for these fine quality Pearls and offer up a range of shapes, sizes and color hues that allow for an incredible variety of designs for our finished pieces. From classic Pearl necklaces and effortlessly wearable Pearl bracelets to dainty rings and highly stylish pendants, the Pearl is timeless; it never goes out of fashion, its elegance transcends generations, and we’re very proud of all of the different Pearl collections we can offer here at Gemporia.

HOW TO CLEAN PEARL

Despite originating underwater, it is recommended not to submerge Pearls in water when caring for them. Use a very soft lint-free microfiber cloth to gently clean your Pearls, and if you need a little help, dampen part of the cloth with some mildly soapy lukewarm water. Dry them immediately with a dry part of the cloth. Don't use any heat to dry excess moisture, though you can gently blow on them to evaporate any extra liquid. Pay particular attention to the string if cleaning necklaces and bracelets - keep it as dry as you can. Don't take your Pearls anywhere near a steam or ultrasonic cleaner.

HOW TO CARE FOR PEARL

Pearl comes in at between 2.5 and 4.5 on the Mohs scale, making it arguably the most popular gemstone to be so low on the famed hardness table - even a fingernail can be hard enough to scratch a Pearl at the bottom end of this range. However, taking good care of your Pearls isn't at all laborious. The most important thing to do when wearing Pearls is to put them on last when you're getting ready as they're vulnerable to all sorts of cosmetic products such as perfume and hairspray. Likewise, take them off first when you're done. Keep them safely stored away from other jewelry and away from extreme temperature and light sources. Give them a wipe down with a soft cloth after wearing, and if you have Pearls that are strung in a necklace or bracelet remember to periodically check the state of the threading material. They may occasionally need re-stringing. When cared for properly, Pearls will last a lifetime. Wear them often as the body’s natural oils help maintain the Pearl’s orient (pearlescence).


References:

https://www.gemporia.com

Sporting Dogs