WINDOWS XP TRICKS

Notepad Secrets

Create a log Open Notepad On the very first line, type in ".LOG" (without quotes) then press Enter for a new line Now you can type in some text if you want, then save the file. Next time when you open the file, notice its contents. Notepad automatically adds a time/date log everytime you open the file. Text becomes unreadable Open Notepad On the very first line, type in "dont eat the donut" (without quotes) then save and close the file. Note: the file should have only one line of the text above. Now, open the same file. You'll notice the text becomes unreadable squares. (try this with different text with the same format and length).

Paint Secrets

Create a trail image Open Paint, then open an image. Right-click on the image and select "Select All" Now hold the "Shift" key and move the image around. The image will be drawn with trail. 10x Zoom Open Paint, then open a small image. Select the zoom "Magnifier" tool. Windows Paint lists out the zoom options from 1x to 8x, but there is a 10x... After selecting the Magnifier tool, point the mouse right at the border line right under "8x" and left-click. There you go.. the hidden 10x.

Other Secrets

Where is the relaxing music that you've heard during Windows XP Installation? The file is in: C:\Windows\system32\oobe\images\title.wma

want to try something cool

Try ths one!...
1)Go to Google,and type 'Google Gravity'...
2) Click on the 1st result that appears i.e. Google Gravity
3) Wait 2 seconds. Something cool will happen! ... ...
4) Then write any word in the Google search bar and press Enter.
5) Wait and see. Something even cooler will happen
6) Play with it for a while.:-)

ARTIFICIAL INTELLIGENCE

Introduction to Artificial Intelligence

Defining Artificial Intelligence

The phrase Artificial Intelligence I, which was coined by John McCarthy three decades ago, evades a concise and formal definition to date. One representative definition is pivoted around the comparison of intelligence of computing machines with human beings . Another definition is concerned with the performance of machines which "historically have been judged to lie within the domain of intelligence" . None of these definitions or the like have been universally accepted, perhaps because of their references to the word "intelligence", which at present is an abstract and immeasurable quantity. A better definition of artificial intelligence, therefore, calls for formalization of the term "intelligence". Psychologist and Cognitive theorists are of the opinion that intelligence helps in identifying the right piece of knowledge at the appropriate instances of decision making. The phrase "artificial intelligence" thus c bane defined as the simulation of human intelligence on a machine, so make the machine efficient to identify and use the right place of "Knowledge" at a given step of solving a problem. A system capable of planning and executing the right task at the right time is generally called rational . Thus, AI alternatively may be stated as a subject dealing with computational models that can think and act rationally 1, 2, 3, 4. A common question then naturally arises: Does rational thinking and acting include all possible characteristics of an intelligent system? If so, how does it represent behavioral intelligence such as machine learning, perception and planning? A little thinking, however, reveals that a system that can reason well must be a successful planner, as planning in many circumstances is part of a reasoning process. Further, a system can act rationally only after acquiring adequate knowledge from the real world. So, perception that stands for building up of knowledge from real world information is a prerequisite feature for rational actions. One step further thinking envisages that a machine without learning capability cannot possess perception. The rational action of an agent (actor), thus, calls for possession of all the elementary characteristics of intelligence. Relating artificial intelligence with the computational models capable of thinking and acting rationally, therefore, has a pragmatic significance.

General Problem Solving Approaches in AI

To understand what exactly artificial intelligence is, we illustrate some common problems. Problems dealt with in artificial intelligence generally use a common term called 'state'. A state represents a status of the solution at a given step of the problem solving procedure. The solution of a problem, thus, is a collection of the problem states. The problem solving procedure applies an operator to a state to get the next state. Then it applies another operator to the resulting state to derive a new state. The process of applying an operator to a state and its subsequent transition to the next state, thus, is continued until the goal (desired) state is derived. Such a method of solving a problem is generally referred to as state space approach. We will first discuss the state-space approach for problem solving by a well-known problem, which most of us perhaps have solved in our childhood.
Example: Consider a 4-puzzle problem, where in a 4-cell board there are 3 cells filled with digits and 1 blank cell. The initial state of the game represents a particular orientation of the digits in the cells and the final state to be achieved is another orientation supplied to the game player. The problem of the game is to reach from the given initial state to the goal (final) state, if possible, with a minimum of moves. Let the initial and the final state be as shown in figures 1(a) and (b) respectively.

Fig.: The initial and the final states of the Number Puzzle game, where B denotes the blank space.
We now define two operations, blank-up (BU) / blank-down (BD) and blank-left (BL) / blank-right (BR), and the state-space (tree) for the problem is presented below using these operators. The algorithm for the above kind of problems is straightforward. It consists of three steps, described by steps 1, 2(a) and 2(b) below.
Algorithm for solving state-space problems
Begin

• state: = initial-state; existing-state:=state;
• While state ≠ final state do
  Begin
  • a. Apply operations from the set {BL, BR, BU, BD} to each state so as to generate new-states;
  • b. If new-states ∩ the existing-states ≠ φ

Then do

• Begin state := new-states - existing-states;
• Existing-states := existing-states - {states}
• End;
• End while;

End.

Fig.: The state-space for the Four-Puzzle problem. It is thus clear that the main trick in solving problems by the state-space approach is to determine the set of operators and to use it at appropriate states of the problem.
Researchers in artificial intelligence have segregated the AI problems from the non-AI problems. Generally, problems, for which straightforward mathematical / logical algorithms are not readily available and which can be solved by intuitive approach only, are called AI problems. The 4-puzzle problem, for instance, is an ideal AI Problem. There is no formal algorithm for its realization, i.e., given a starting and a goal state, one cannot say prior to execution of the tasks the sequence of steps required to get the goal from the starting state. Such problems are called the ideal AI problems. The wellknown water-jug problem, the Travelling Salesperson Problem (TSP) , and the n-Queen problem are typical examples of the classical AI problems. Among the non-classical AI problems, the diagnosis problems and the pattern classification problem need special mention. For solving an AI problem, one may employ both artificial intelligence and non-AI algorithms. An obvious question is: what is an AI algorithm? Formally speaking, an artificial intelligence algorithm generally means a non-conventional intuitive approach for problem solving. The key to artificial intelligence approach is intelligent search and matching. In an intelligent search problem / sub-problem, given a goal (or starting) state, one has to reach that state from one or more known starting (or goal) states.
For example, consider the 4-puzzle problem, where the goal state is known and one has to identify the moves for reaching the goal from a pre-defined starting state. Now, the less number of states one generates for reaching the goal, the better is the AI algorithm.
The question that then naturally arises is: how to control the generation of states. This, in fact, can be achieved by suitably designing some control strategies, which would filter a few states only from a large number of legal states that could be generated from a given starting / intermediate state. As an example, consider the problem of proving a trigonometric identity that children are used to doing during their schooldays. What would they do at the beginning? They would start with one side of the identity, and attempt to apply a number of formulae there to find the possible resulting derivations. But they won't really apply all the formulae there. Rather, they identify the right candidate formula that fits there best, such that the other side of the identity seems to be closer in some sense (outlook). Ultimately, when the decision regarding the selection of the formula is over, they apply it to one side (say the L.H.S) of the identity and derive the new state. Thus they continue the process and go on generating new intermediate states until the R.H.S (goal) is reached. But do they always select the right candidate formula at a given state? From our experience, we know the answer is "not always". But what would we do if we find that after generation of a few states, the resulting expression seems to be far away from the R.H.S of the identity. Perhaps we would prefer to move to some old state, which is more promising, i.e., closer to the R.H.S of the identity. The above line of thinking has been realized in many intelligent search problems of AI. Some of these well-known search algorithms are:

• Generate and Test
• Hill Climbing
• Heuristic Search
• Means and Ends analysis

a) Generate and Test Approach: This approach concerns the generation of the state-space from a known starting state (root) of the problem and continues expanding the reasoning space until the goal node or the terminal state is reached. In fact after generation of each and every state, the generated node is compared with the known goal state. When the goal is found, the algorithm terminates. In case there exist multiple paths leading to the goal, then the path having the smallest distance from the root is preferred. The basic strategy used in this search is only generation of states and their testing for goals but it does not allow filtering of states.
(b) Hill Climbing Approach: Under this approach, one has to first generate a starting state and measure the total cost for reaching the goal from the given starting state. Let this cost be f. While f = a predefined utility value and the goal is not reached, new nodes are generated as children of the current node. However, in case all the neighborhood nodes (states) yield an identical value of f and the goal is not included in the set of these nodes, the search algorithm is trapped at a hillock or local extrema. One way to overcome this problem is to select randomly a new starting state and then continue the above search process. While proving trigonometric identities, we often use Hill Climbing, perhaps unknowingly.
(c) Heuristic Search: Classically heuristics means rule of thumb. In heuristic search, we generally use one or more heuristic functions to determine the better candidate states among a set of legal states that could be generated from a known state. The heuristic function, in other words, measures the fitness of the candidate states. The better the selection of the states, the fewer will be the number of intermediate states for reaching the goal. However, the most difficult task in heuristic search problems is the selection of the heuristic functions. One has to select them intuitively, so that in most cases hopefully it would be able to prune the search space correctly. We will discuss many of these issues in a separate chapter on Intelligent Search.
(d) Means and Ends Analysis: This method of search attempts to reduce the gap between the current state and the goal state. One simple way to explore this method is to measure the distance between the current state and the goal, and then apply an operator to the current state, so that the distance between the resulting state and the goal is reduced. In many mathematical theorem- proving processes, we use Means and Ends Analysis.

The Disciplines of Artificial Intelligence

The subject of artificial intelligence spans a wide horizon. It deals with the various kinds of knowledge representation schemes, different techniques of intelligent search, various methods for resolving uncertainty of data and knowledge, different schemes for automated machine learning and many others. Among the application areas of AI, we have Expert systems, Game-playing, and Theorem-proving, Natural language processing, Image recognition, Robotics and many others. The subject of artificial intelligence has been enriched with a wide discipline of knowledge from Philosophy, Psychology, Cognitive Science, Computer Science, Mathematics and Engineering. Thus in fig. , they have been referred to as the parent disciplines of AI. An at-a-glance look at fig. also reveals the subject area of AI and its application areas.

Fig.: AI, its parent disciplines and application areas.

The Subject of Artificial Intelligence

The subject of artificial intelligence was originated with game-playing and theorem-proving programs and was gradually enriched with theories from a number of parent disciplines. As a young discipline of science, the significance of the topics covered under the subject changes considerably with time. At present, the topics which we find significant and worthwhile to understand the subject are outlined below:

FigA: Pronunciation learning of a child from his mother.
Learning Systems: Among the subject areas covered under artificial intelligence, learning systems needs special mention. The concept of learning is illustrated here with reference to a natural problem of learning of pronunciation by a child from his mother (vide figA). The hearing system of the child receives the pronunciation of the character "A" and the voice system attempts to imitate it. The difference of the mother's and the child's pronunciation, hereafter called the error signal, is received by the child's learning system auditory nerve, and an actuation signal is generated by the learning system through a motor nerve for adjustment of the pronunciation of the child. The adaptation of the child's voice system is continued until the amplitude of the error signal is insignificantly low. Each time the voice system passes through an adaptation cycle, the resulting tongue position of the child for speaking "A" is saved by the learning process. The learning problem discussed above is an example of the well-known parametric learning, where the adaptive learning process adjusts the parameters of the child's voice system autonomously to keep its response close enough to the "sample training pattern". The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. Besides this type, the other common learning methods, which we do unknowingly, are inductive and analogy-based learning. In inductive learning, the learner makes generalizations from examples. For instance, noting that "cuckoo flies", "parrot flies" and "sparrow flies", the learner generalizes that "birds fly". On the other hand, in analogy-based learning, the learner, for example, learns the motion of electrons in an atom analogously from his knowledge of planetary motion in solar systems.
Knowledge Representation and Reasoning: In a reasoning problem, one has to reach a pre-defined goal state from one or more given initial states. So, the lesser the number of transitions for reaching the goal state, the higher the efficiency of the reasoning system. Increasing the efficiency of a reasoning system thus requires minimization of intermediate states, which indirectly calls for an organized and complete knowledge base. A complete and organized storehouse of knowledge needs minimum search to identify the appropriate knowledge at a given problem state and thus yields the right next state on the leading edge of the problem-solving process. Organization of knowledge, therefore, is of paramount importance in knowledge engineering. A variety of knowledge representation techniques are in use in Artificial Intelligence. Production rules, semantic nets, frames, filler and slots, and predicate logic are only a few to mention. The selection of a particular type of representational scheme of knowledge depends both on the nature of applications and the choice of users.
Planning: Another significant area of artificial intelligence is planning. The problems of reasoning and planning share many common issues, but have a basic difference that originates from their definitions. The reasoning problem is mainly concerned with the testing of the satisfiability of a goal from a given set of data and knowledge. The planning problem, on the other hand, deals with the determination of the methodology by which a successful goal can be achieved from the known initial states. Automated planning finds extensive applications in robotics and navigational problems, some of which will be discussed shortly.
Knowledge Acquisition: Acquisition (Elicitation) of knowledge is equally hard for machines as it is for human beings. It includes generation of new pieces of knowledge from given knowledge base, setting dynamic data structures for existing knowledge, learning knowledge from the environment and refinement of knowledge. Automated acquisition of knowledge by machine learning approach is an active area of current research in Artificial Intelligence. Intelligent Search: Search problems, which we generally encounter in Computer Science, are of a deterministic nature, i.e., the order of visiting the elements of the search space is known. For example, in depth first and breadth first search algorithms, one knows the sequence of visiting the nodes in a tree. However, search problems, which we will come across in AI, are non-deterministic and the order of visiting the elements in the search space is completely dependent on data sets. The diversity of the intelligent search algorithms will be discussed in detail later.
Logic Programming: For more than a century, mathematicians and logicians were used to designing various tools to represent logical statements by symbolic operators. One outgrowth of such attempts is propositional logic, which deals with a set of binary statements (propositions) connected by Boolean operators. The logic of propositions, which was gradually enriched to handle more complex situations of the real world, is called predicate logic. One classical variety of predicate logic-based programs is Logic Program. PROLOG, which is an abbreviation for PROgramming in LOGic, is a typical language that supports logic programs. Logic Programming has recently been identified as one of the prime area of research in AI. The ultimate aim of this research is to extend the PROLOG compiler to handle spatio-temporal models and support a parallel programming environment. Building architecture for PROLOG machines was a hot topic of the last decade.
Soft Computing: Soft computing, according to Prof. Zadeh, is "an emerging approach to computing, which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision" . It, in general, is a collection of computing tools and techniques, shared by closely related disciplines that include fuzzy logic, artificial neural nets, genetic algorithms, belief calculus, and some aspects of machine learning like inductive logic programming. These tools are used independently as well as jointly depending on the type of the domain of applications.
Management of Imprecision and Uncertainty: Data and knowledgebases in many typical AI problems, such as reasoning and planning, are often contaminated with various forms of incompleteness. The incompleteness of data, hereafter called imprecision, generally appears in the database for i) lack of appropriate data, and ii) poor authenticity level of the sources. The incompleteness of knowledge, often referred to as uncertainty, originates in the knowledge base due to lack of certainty of the pieces of knowledge Reasoning in the presence of imprecision of data and uncertainty of knowledge is a complex problem. Various tools and techniques have been devised for reasoning under incomplete data and knowledge. Some of these techniques employ i) stochastic ii) fuzzy and iii) belief network models. In a stochastic reasoning model, the system can have transition from one given state to a number of states, such that the sum of the probability of transition to the next states from the given state is strictly unity. In a fuzzy reasoning system, on the other hand, the sum of the membership value of transition from the given state to the next state may be greater than or equal to one. The belief network model updates the stochastic / fuzzy belief assigned to the facts embedded in the network until a condition of equilibrium is reached, following which there would be no more change in beliefs. Recently, fuzzy tools and techniques have been applied in a specialized belief network, called a fuzzy Petri net, for handling both imprecision of data and uncertainty of knowledge by a unified approach.

Applications of Artificial Intelligence Techniques

Almost every branch of science and engineering currently shares the tools and techniques available in the domain of artificial intelligence. However, for the sake of the convenience of the readers, we mention here a few typical applications, where AI plays a significant and decisive role in engineering automation.
Expert Systems: In this example, we illustrate the reasoning process involved in an expert system for a weather forecasting problem with special emphasis to its architecture. An expert system consists of a knowledge base, database and an inference engine for interpreting the database using the knowledge supplied in the knowledge base. The reasoning process of a typical illustrative expert system is described in Fig. PR 1 in Fig. represents i-th production rule. The inference engine attempts to match the antecedent clauses (IF parts) of the rules with the data stored in the database. When all the antecedent clauses of a rule are available in the database, the rule is fired, resulting in new inferences. The resulting inferences are added to the database for activating subsequent firing of other rules. In order to keep limited data in the database, a few rules that contain an explicit consequent (THEN) clause to delete specific data from the databases are employed in the knowledge base. On firing of such rules, the unwanted data clauses as suggested by the rule are deleted from the database. Here PR1 fires as both of its antecedent clauses are present in the database. On firing of PR1, the consequent clause "it-will-rain" will be added to the database for subsequent firing of PR2.

Fig. Illustrative architecture of an expert system.
Image Understanding and Computer Vision: A digital image can be regarded as a two-dimensional array of pixels containing gray levels corresponding to the intensity of the reflected illumination received by a video camera. For interpretation of a scene, its image should be passed through three basic processes: low, medium and high level vision .

Fig.: Basic steps in scene interpretation.
The importance of low level vision is to pre-process the image by filtering from noise. The medium level vision system deals with enhancement of details and segmentation (i.e., partitioning the image into objects of interest ). The high level vision system includes three steps: recognition of the objects from the segmented image, labeling of the image and interpretation of the scene. Most of the AI tools and techniques are required in high level vision systems. Recognition of objects from its image can be carried out through a process of pattern classification, which at present is realized by supervised learning algorithms. The interpretation process, on the other hand, requires knowledge-based computation.
Speech and Natural Language Understanding: Understanding of speech and natural languages is basically two class ical problems. In speech analysis, the main probl em is to separate the syllables of a spoken word and determine features like ampli tude, and fundamental and harmonic frequencies of each syllable. The words then could be ident ified from the extracted features by pattern class ification techniques. Recently, artificial neural networks have been employed to class ify words from their features. The probl em of understanding natural languages like English, on the other hand, includes syntactic and semantic interpretation of the words in a sentence, and sentences in a paragraph. The syntactic steps are required to analyze the sentences by its grammar and are similar with the steps of compilation. The semantic analysis, which is performed following the syntactic analysis, determines the meaning of the sentences from the association of the words and that of a paragraph from the closeness of the sentences. A robot capable of understanding speech in a natural language will be of immense importance, for it could execute any task verbally communicated to it. The phonetic typewriter, which prints the words pronounced by a person, is another recent invention where speech understanding is employed in a commercial application.
Scheduling: In a scheduling problem, one has to plan the time schedule of a set of events to improve the time efficiency of the solution. For instance in a class-routine scheduling problem, the teachers are allocated to different classrooms at different time slots, and we want most classrooms to be occupied most of the time. In a flowshop scheduling problem, a set of jobs J1 and J2 (say) are to be allocated to a set of machines M1, M2 and M3. (say). We assume that each job requires some operations to be done on all these machines in a fixed order say, M1, M2 and M3. Now, what should be the schedule of the jobs (J1-J2) or (J2 -J1), so that the completion time of both the jobs, called the make-span, is minimized? Let the processing time of jobs J1 and J2 on machines M1, M2 and M3 be (5, 8, 7) and (8, 2, 3) respectively. The gantt charts in fig. (a) and (b) describe the make-spans for the schedule of jobs J1 - J2 and J2 - J1 respectively. It is clear from these figures that J1-J2 schedule requires less make-span and is thus preferred.


Fig.: The Gantt charts for the flowshop scheduling problem with 2 jobs and 3 machines.
Flowshop scheduling problems are a NP complete problem and determination of optimal scheduling (for minimizing the make-span) thus requires an exponential order of time with respect to both machine-size and job-size. Finding a sub-optimal solution is thus preferred for such scheduling problems. Recently, artificial neural nets and genetic algorithms have been employed to solve this problem. The heuristic search, to be discussed shortly, has also been used for handling this problem.
Intelligent Control: In process control, the controller is designed from the
known models of the process and the required control objective. When the
dynamics of the plant is not completely known, the existing techniques for
controller design no longer remain valid. Rule-based control is appropriate in
such situations. In a rule-based control system, the controller is realized by a
set of production rules intuitively set by an expert control engineer. The
antecedent (premise) part of the rules in a rule-based system is searched
against the dynamic response of the plant parameters. The rule whose
antecedent part matches with the plant response is selected and fired. When
more than one rule is firable, the controller resolves the conflict by a set of
strategies. On the other hand, there exist situations when the antecedent part
of no rules exactly matches with the plant responses. Such situations are
handled with fuzzy logic, which is capable of matching the antecedent parts of
rules partially/ approximately with the dynamic plant responses. Fuzzy control has been successfully used in many industrial plants. One typical
application is the power control in a nuclear reactor. Besides design of the
controller, the other issue in process control is to design a plant (process)
estimator, which attempts to follow the response of the actual plant, when
both the plant and the estimator are jointly excited by a common input signal.
The fuzzy and artificial neural network-based learning techniques have recently
been identified as new tools for plant estimation.

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INTERVIEW TIPS

Q: What job would you like to have in five years time?

A: It's a mistake if you haven't thought about this question and how to answer it. Your interviewer will use your response to gauge your desired career direction, your ability to plan out how to achieve your goal and what moves you are making to reach it. It will also tell them how real your desire to reach your goal is and to what extent their organization can accommodate your aspirations. A good answer will include the following: an acknowledgement from you that you have things to learn; that you would like the company to help you with this learning; and that although you have progression within this department in your sights you will also consider other opportunities throughout the company, should they arise.

Q: Why do you want to work here?

A: This requires some forethought. It means that you go into an interview forearmed with facts and information about the company you are looking for a job with. If you've done your homework you have nothing to fear. Your reply should include the company's attributes as you see them and why these attributes will bring out the best in you.

Q: How do you work under pressure?

A: This question is offering you the opportunity to sell your skills to your prospective employer. Think of an example in your current job, explain how it arose and how you dealt with it. Do not say anything negative about yourself unless you can finish off your reply with what you have learned from the experience. You can also use this question to demonstrate how you can alleviate pressured work situations arising - that your own capabilities to plan and manage your time can reduce hasty decisions and panicked deadlines arising.

Q: Why do you want to leave your current job?

A: The acceptable answers to this question fall into two categories, how you feel about your career and how you feel about the company you currently work for. And your answer may include a combination of reasons from both areas. Regarding your career - do you want fresh challenges? More opportunity for growth? Would you like to develop new skills? With regard to the company, did you feel that your position was not secure? Was there nowhere else for you to go in the department? Does the company you are applying for a job with have a better reputation?

Q: What specifically do you have to offer us?

A: Start your answer with a recap of the job description of the post you are applying for, then meet it point by point with your skills. It's important that you also paint a picture of yourself as a problem solver, someone who can take direction and who is a team player, and of course someone who is not only interested in their personal career success, but the success of the company.

Q: What is your greatest weakness?

A: This question is an attempt by the interviewer to tempt you into casting yourself in a negative light - don't do it. Always turn your weaknesses into positives, and keep your answer general. Try to think about allowable weaknesses for example, a lack of knowledge in a certain area is an opportunity for development. Frustration with others may signal your total commitment to a project or a perfectionist nature.

Q: What are your greatest accomplishments?

A: Keep your answer to this question job related, think of past projects or initiatives which you have played a part in and which have brought positive results for you and the company. Do not exaggerate your role, if your greatest achievement occurred as part of a team, then say so. It not only shows your ability to work with others, but to share credit when credit is due.

Q: How do you handle criticism?

A: This question is designed to find out if you are manageable as an employee. In your answer you must present yourself as someone who will accept direction, but who also has a decent quotient of self-respect. Everyone deserves a reason and explanation for criticism. If your manager does this in a way which respects your situation then you should say that this is acceptable to you and that you will grow from it. If the correction is brusque, you should also say that you can accept this too - your boss may have something on his/her mind, you must show yourself as someone who can recognize the bigger picture.

Q: Are you willing to travel/relocate?

A: In this question you may find yourself caught between a rock and a hard place. If you say no - you might as well end the interview there and then. If you say yes, who is to say you won't end up in Alaska? Find out what they are really asking - is it business travel or is the company relocating? If relocation is the option there are two ways to play this - be honest, or veer towards yes whatever, after all your goal at interview is to get a job offer, without such an offer you have no decision to make, and no chips to bargain with.

Q: What kind of people does you like working with?

A: Easy, people like you! In this question you can again flag up your attributes as attributes you expect in others. Key words are pride, dedication, self-respect and honesty.

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