Fluent 14 Theory Zip
Niccoletta Boyer
My problem is that I have great troubles to write them on a music sheet. I am not fluent at all. I don't know what note it is that I have in my mind. A few months ago, I downloaded the software Musescore to help me create the sheets. The great thing is that it allows me to recognize one note at a time by ear... but I am very slow with this process. And I can't describe how frustrating it is.
The processing fluency theory of aesthetic pleasure[1] is a theory in psychological aesthetics on how people experience beauty. Processing fluency is the ease with which information is processed in the human mind.
The processing fluency theory of aesthetic pleasure emphasizes the interaction between the viewer and an object in that it integrates theories and a wide range of empirical evidence that focus on effects of objective stimulus attributes on perceived beauty[5] with those that emphasize the role of experience, for example by invoking prototypicality.[6] In this theory, beauty is seen as an experience that has nothing to do with artistic merit: Beautiful works of art may be without any merit whereas good art is not necessarily beautiful.
The theory resolves the apparent paradox of inborn and acquired preferences. For instance, infants prefer consonant melodies. According to the fluency account, this is because infants share perceptual equipment that make them process consonance in music more easily than dissonance. When children grow up, they are exposed to the music of their culture, resulting in culture-specific musical fluency. This familiarization explains why individuals from different cultures have different musical tastes. In addition, the theory helps explain why beauty (in a wide sense; perhaps the term elegance is more apt) is a cue for truth in mathematical problem solving and scientific discovery.[7][8]
Leaders who want to innovate management theory and practice, particularly in small- to mid-size organizations, may find the Strengthening zone to be the best fit for their teams. This zone is a possible future of agile. Cutting-edge agile practice appears to be moving in that direction. However, be cautioned that this zone requires researching cutting-edge management theory and inventing new ways of working.
Fluency is more a matter of habits than skills. Although training can teach the underlying techniques, the skillful ease at the heart of fluent proficiency requires deliberate, thoughtful, day-in-day-out practice over months. It comes from a deliberate investment in learning through practice.
One of the biggest investments the organization will make is time. True fluency takes longer than anyone expects or wants. In our experience coaching teams, it takes a team two to six months to become fluent in the Focusing zone. Reaching the Delivering zone takes another 3-24 months, depending on the amount of technical debt in the code. Optimizing fluency can take another 1-5 years, depending on organizational trust and willingness to change reporting structures.
This zone is most appropriate for organizations whose leaders want to be on the cutting edge of management theory and practice. It requires working at the leading edge of organizational theory and inventing new ways of applying it to agile teams.
So, how can we write better APIs? Besides, of course, proper documentation, the usability of an API is greatly influenced by its design. In this post we discuss fluent APIs, which are produced by a funky and popular design technique. We learn what fluent API is, how it benefits the programmer, and how to compose expressive fluent APIs. We also show the applicability of fluent APIs to domain-specific languages.
Fluent APIs are usually associated with object-oriented programming languages. Although we mostly present Java code excerpts, equivalent fluent APIs can be composed in any OO programming language, like C#, Go, Scala, and others.
Although method append() does not produce anything, it does return the this object, for the sole purpose of allowing fluent calls. As method append() returns a StringBuilder, its invocation can be immediately followed by another StringBuilder method call, and so on. Finally, method toString() concludes the chain by returning a string instance, achieving the goal of the StringBuilder.
A fluent API contributes to code readability by allowing multiple statements to be abbreviated to a single expression. For instance, by replacing the imperative StringBuilder method calls above with a fluent chain, we made the code much shorter, and we removed the need for the StringBuilder variable.
Broadly speaking, to make a fluent API for class Fluent, let its methods return the type Fluent; methods that return other types (like toString()) are meant to seal the fluent chains, and optionally yield their computation results. But what objects do the fluent methods actually return? Like with StringBuilder, and builders in general, having the fluent methods return the current object is the simplest way to go.
In fluent APIs that follow this recipe, the imperative and fluent call styles are interchangeable. This may not be the case, however, if the fluent methods return new API instances: return new Fluent(); instead of return this;. API instantiation can be used to make the fluent chains immutable, or it can simply be required by the implementation. To avoid confusion, the correct way to use a fluent API is usually explained in its documentation.
While fluent APIs can be implemented in dynamic programming languages (like Python and JavaScript), they are especially potent in static languages (like Java and C++). One reason may be that the syntax of static languages tends to be more strict and cumbersome, so fluent chains spare the programmer a bit more hassle. Yet, the truly fascinating feature of fluent APIs in static languages is their ability to enforce API protocols at compile-time.
Nevertheless, fluent APIs in static programming languages can detect API abuses as early as compilation time. For example, our mail API forbids a call to from() to be followed by a subject() call, as it skips the senders field (to()). While designing the API, we can ensure that the return type of from() does not contain the method subject(); that way, a chain containing from().subject(), which breaks the protocol, cannot even compile, as the subject() call cannot be resolved statically.
After representing the protocol with an FSM, we can directly implement it in a fluent API. We turn every state into a class, and each edge into a method; given an edge labeled f from state X to state Y, we add method f() to class X and give the method the return type Y. To every accepting state, we also add terminating methods (build()).
Besides the brute enforcement of a protocol, a fluent API also integrates neatly with certain IDE services. Auto-completion is a common IDE feature that suggests possible code continuations. When applied to a fluent API, auto-completion actively guides the programmer through the API, by showing what calls may continue the chain, while ignoring methods that would break it.
Unfortunately, the fluent API implementation may end up messy and unmaintainable, because it is composed of multiple classes. If two FSM edges have the same label, then we have to encode them as identical methods in two different API classes. Also, as fluent methods may not return the class they are contained in, they have to instantiate a new object, and pass it any intermediate data collected in the chain. Both of these problems can be solved by encoding fluent API states as interfaces, rather than classes. Then, we create a single master class that implements all interfaces, so its methods can simply return the this instance. That way, we not only concentrate the API logic in a single class, but we also avoid multiple object instantiations in a single chain. This alternative design still gives the benefits of a fluent API, assuming the master class is not visible to the user.
A fluent API is a neat solution for embedding a DSL. If the syntax of the DSL can be defined as an FSM, then we can turn it into a fluent API using the method described above and catch syntax errors at compile time. We encode DSL keywords as methods and DSL literals (like numbers and strings) and expressions as parameters to these methods:
A fluent API is invoked by a chain of consecutive method calls. It is a trendy practice for making elegant and concise interfaces. In this post we learned how to implement basic fluent APIs, showed that they can enforce API contracts at compile-time, and saw that they can even be used to embed domain-specific languages.
The ACM Special Interest Group on Programming Languages (SIGPLAN) explores programming language concepts and tools, focusing on design, implementation, practice, and theory. Its members are programming language developers, educators, implementers, researchers, theoreticians, and users.
A theory has been proposed recently that asserts that the problem that people who stutter have arises when these speakers attempt to execute speech at a faster rate than planning processes allow. This leads speakers to complete words before the following one is ready. Plan unavailability usually happens on the more complex content words rather than the relatively simple function words. There are two ways of dealing with this situation when it arises. Speakers can (1) delay production of a content word by repeating prior function words or (2) carry on and attempt to produce the following content word and gamble that the remainder of the plan arrives while it is being executed. The former strategy does not lead speakers to persist in their dysfluency, while the latter does. It is proposed that the pressure on speech rate that leads speakers to adopt the latter strategy is particularly acute around adolescence. In this article two experiments are reported which test the effects of rate on fluency. In Experiment 1, fluent speakers are induced to produce stuttering-like dysfluencies on content words using a commentary task. A prediction of the theory is that procedures known to induce fluency have to produce local slowing of speech so that planning and execution can get back in synchrony. This prediction is confirmed for frequency-shifted feedback and when speakers who stutter have to sing in Experiment 2. Results are discussed in terms of the model for the etiology of stuttering based on plan unavailability. The implications of the results are also discussed with respect to the diagnosis of the disorder and how it can be treated.
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