Rails' observers were partly inspired by aspect-oriented programming -- they're about cross-cutting concerns. If you're putting business logic in observers that is tightly coupled to the models they're observing, you're doing it wrong IMO.

Asking questions ensures they fully understand whatever it is they’re doing. They don’t go into projects blindly or assume anything. They ask probing questions to gain a complete understanding of what it is they’re trying to accomplish, why they’re working towards that goal, and everything else in between. Having an analytical mind ensures that they don’t let any details slip through the cracks.

Some may mistake their numerous, detailed questions as a trait of a perfectionist, which can be the case, but not always. Accuracy can be misinterpreted as perfection. If you’re detail-oriented, don’t let the fear of appearing as a perfectionist keep you from doing quality work.

A system is a composition of objects thatare abstractions, which hide data andexpose behavior*

You should take care, however, to make sure that your individual objects can stand alone as much as possible. Tightly coupled objects are objects that rely so heavily on each other that removing or changing one will mean that you have to completely change another one - a real bummer.

An object in object-oriented language is essentially a record that contains procedures specialized to handle that record; and object types are an elaboration of record types. Indeed, in most object-oriented languages, records are just special cases of objects, and are known as plain old data structures (PODSs), to contrast with objects that use OO features.

an object-oriented approach to data modelling – where data is described in terms of classes, attributes, and associations

42 E 32 * Nischengrab, Wandgrab

If we would have kept the call to super out of the #initialize_dup method, we would never have called initialize_copy, so it is important to keep that in.

Object hierarchies are very different from relational hierarchies. Relational hierarchies focus on data and its relationships, whereas objects manage not only data, but also their identity and the behavior centered around that data.

Since around 2010, Morton has become associated with a philosophical movement known as object-oriented ontology, or O.O.O. The point of O.O.O. is that there is a vast cosmos out there in which weird and interesting shit is happening to all sorts of objects, all the time. In a 1999 lecture, “Object-Oriented Philosophy,” Graham Harman, the movement’s central figure, explained the core idea:The arena of the world is packed with diverse objects, their forces unleashed and mostly unloved. Red billiard ball smacks green billiard ball. Snowflakes glitter in the light that cruelly annihilates them, while damaged submarines rust along the ocean floor. As flour emerges from mills and blocks of limestone are compressed by earthquakes, gigantic mushrooms spread in the Michigan forest. While human philosophers bludgeon each other over the very possibility of “access” to the world, sharks bludgeon tuna fish and icebergs smash into coastlines.We are not, as many of the most influential twentieth-century philosophers would have it, trapped within language or mind or culture or anything else. Reality is real, and right there to experience—but it also escapes complete knowability. One must confront reality with the full realization that you’ll always be missing something in the confrontation. Objects are always revealing something, and always concealing something, simply because they are Other. The ethics implied by such a strangely strange world hold that every single object everywhere is real in its own way. This realness cannot be avoided or backed away from. There is no “outside”—just the entire universe of entities constantly interacting, and you are one of them.

(Always call super to inherit the default behavior.)

Adding Object Oriented Principles (OOP) to a functional codebase adds yet another way of writing code, reducing consistency and clarity.

For instance, those who prefer classical inheritance may enjoy the addition of the class keyword, while others may reject it as conflicting with the idea of a prototypical inheritance model.

Internally, it creates and returns a fresh, subclassed activity (via patching) whilst replacing the step for given :id. Be advised that this does not change the original activity class.

Remember, in a railway activity each task has two standard outputs with the “semantics” success and failure.

The flow pipetree is a mix of the Either monad and “Railway-oriented programming”, but not entirely the same.

The DSL will, per default, wrap every task with the Binary interface, meaning returning true will result in Activity::Right, and false in Activity::Left.

Note that you can return either a boolean value or a [signal subclass] in order to dictate the direction of flow.

Each step receives the return value of its predecessor. The return value decides about what next step is called.

the ability to “error out” when something goes wrong

Six lines of code create an executable object that, when invoked, will run your code in the order as visible in our diagram, plus the ability to “error out” when something goes wrong.

While you could program this little piece of logic and flow yourself using a bunch of Ruby methods along with a considerable amount of ifs and elses, and maybe elsif, if you’re feeling fancy, a Trailblazer activity provides you a simple API for creating such flow without having to write and maintain any control code. It is an abstraction.

Modelling the flow of a program where chunks of code are executed in a certain order, with a successful “happy path” and an “error-out” path is called a Railway. It popped up in functional languages

Let’s start with the same number dividing example, which returns 0 when the error happens. Maybe instead we can indicate that the result was not successful without any explicit numerical value?

Now you can easily spot them! The rule is: if you see a Result it means that this function can throw an exception. And you even know its type in advance.

Railway Oriented Programming is a way to gracefully handle errors in your application

However, you don’t need to have an extensive knowledge of monads to use ROP in your code.

Railway Oriented Programming comes from functional programming, so it is tightly related to the usual FP concepts like monads, composition, and many others.

It reminds us that rather than asking an object for data and acting on that data, we should instead tell an object what to do.

The .chain() method allows us to switch over to the left track if an error occurs. Note that the switches only go one way.

This is a useful approach to error handling, but please don’t take it to extremes! See my post on “Against Railway-Oriented Programming”.

The central ideas of this design pattern closely mirror the semantics of first-class functions and higher-order functions in functional programming languages. Specifically, the invoker object is a higher-order function of which the command object is a first-class argument.

In object-oriented programming, information hiding (by way of nesting of types) reduces software development risk by shifting the code's dependency on an uncertain implementation (design decision) onto a well-defined interface. Clients of the interface perform operations purely through it so if the implementation changes, the clients do not have to change.

Though getter/setter methods are commonplace in Java, they are not particularly object oriented (OO). In fact, they can damage your code's maintainability. Moreover, the presence of numerous getter and setter methods is a red flag that the program isn't necessarily well designed from an OO perspective.

The changes we needed to make to main to reassign post mean that this implementation doesn’t quite follow the object-oriented state pattern anymore: the transformations between the states are no longer encapsulated entirely within the Post implementation. However, our gain is that invalid states are now impossible because of the type system and the type checking that happens at compile time! This ensures that certain bugs, such as display of the content of an unpublished post, will be discovered before they make it to production.

The educator’s role in self-directed learning

"Objects in a program should be replaceable with instances of their subtypes without altering the correctness of that program." See also design by contract.

some programming languages that provide lexical closures view encapsulation as a feature of the language orthogonal to object orientation.

A paradigm is a model or pattern. In JavaScript, there are a number of popular paradigms including object-oriented programming (OOP) and functional programming (FP). Paradigms are more important than is sometimes recognized. They help form mental models for solving problems. Becoming well-versed in the principles of a given paradigm can help accelerate development by providing mental shortcuts for solving the challenges that arise while building applications. They can also help produce higher quality software.

Java may have been designed as a completely object oriented language, but when Java SE 8 was released in 2014, it added Lambda expressions (aka closures), which added some functional programming elements. Not every problem is best served by OOP, and by adding Lambdas, Java became more flexible. 

Another Ruby gem, Spira, allows graph data to be used as model objects

prototype-based,

object-oriented

He spells out his point by saying:

The goal here is explicitly not to improve the state of the art in the narrow domain of restaurantbooking, but to take a narrow domain where traditional handcrafted dialog systems are known toperform well, and use that to gauge the strengths and weaknesses of current end-to-end systemswith no domain knowledge

Unsurprisingly, perfectly coded rule-based systems can solve the simulated tasks T1-T5 perfectly,whereas our machine learning methods cannot. However, it is not easy to build an effective rule-based

SUPERVISEDEMBEDDINGMODELS

We implemented a rule-based system for this task in the followingway. We initialized a dialog state using the 3 relevant slots for this task: cuisine type, location andprice range. Then we analyzed the training data and wrote a series of rules that fire for triggers likeword matches, positions in the dialog, entity detections or dialog state, to output particular responses,API calls and/or update a dialog state. Responses are created by combining patterns extracted fromthe training set with entities detected in the previous turns or stored in the dialog state. Overall webuilt 28 rules and extracted 21 patterns. We optimized the choice of rules and their application priority(when needed) using the validation set, reaching a validation per-response accuracy of 40.7%. Wedid not build a rule-based system forConciergedata as it is even less constrained.

Finally, the policy network output is generated bya three-way matrix transformation,

a distributed representationgenerated by an intent network and a probabilitydistribution over slot-value pairs called the beliefstate

In learning such neural network based dialogmodel, we propose hybrid offline training and on-line interactive learning methods. We first let theagent to learn from human-human conversationswith offline supervised training. We then improvethe agent further by letting it to interact with usersand learn from user demonstrations and feedbackwith imitation and reinforcement learning.

We design neural net-work based dialog system that is able to ro-bustly track dialog state, interface with knowl-edge bases, and incorporate structured queryresults into system responses to successfullycomplete task-oriented dialog.

A potential draw-back with such pre-training approach is that themodel may suffer from the mismatch of dialoguestate distributions between supervised training andinteractive learning stages. While interacting withusers, the agent’s response at each turn has a di-rect influence on the distribution of dialogue statethat the agent will operate on in the upcoming di-alogue turns.

These system components areusually trained independently, and their optimiza-tion targets may not fully align with the overallsystem evaluation criteria (e.g. task success rateand user satisfaction). Moreover, errors made inthe upper stream modules of the pipeline propa-gate to downstream components and get amplified,making it hard to track the source of errors

The attentionmechanism later introduced in [12] enables the encoder-decodermodel to learn a soft alignment and to decode at the same time.

We present a general solution towards building task-orienteddialogue systems for online shopping, aiming to assist on-line customers in completing various purchase-related tasks,such as searching products and answering questions, in a nat-ural language conversation manner. As a pioneering work, weshow what & how existing natural language processing tech-niques, data resources, and crowdsourcing can be leveragedto build such task-oriented dialogue systems for E-commerceusage. To demonstrate its effectiveness, we integrate our sys-tem into a mobile online shopping application. To the bestof our knowledge, this is the first time that an dialogue sys-tem in Chinese is practically used in online shopping scenariowith millions of real consumers. Interesting and insightful ob-servations are shown in the experimental part, based on theanalysis of human-bot conversation log. Several current chal-lenges are also pointed out as our future directions

System Formalization

Retrieval-based MethodsRetrieval-based methods choose a response from candidateresponses. The key to retrieval-based methods is message-response matching. Matching algorithms have to overcomesemantic gaps between messages and responses [28].

TASK-ORIENTED DIALOGUESYSTEMSTask-oriented dialogue systems have been an important branchof spoken dialogue systems. In this section, we will reviewpipeline and end-to-end methods for task-oriented dialoguesystems.

2.2 End-to-End Methods

TASK-ORIENTED DIALOGUESYSTEMS

2.1.3 Policy learning

A statistical dialog system

Dialogue State Tracking

Slot filling

Language Understanding

The first step is lexical analysis, i.e. word segmentation and part-of-speech (POS)tagging. The words and POS labels are used as features in the subsequent models. Forthe shared task we used HanLP [1] as our Chinese lexical analyzer.

Each rule is of the form “if thequery q is listed in a particular lexicon L, and the preceding queries and their predicteddomain labels satisfy certain conditions, then q is assigned a certain intent label and,with the exception of short commands, the entire q is regarded as a slot of the typecorresponding to L.” The rules are arranged in sequential order in accordance with theirpriorities

Figure 1 shows the framework of our SLU system, which consists of the context-dependent rules for entity-only queries and the context-independent model for querieswith IISPs. The entire system feeds the query to the rules first. If the rule-based compo‐nent returns null result, that means the query is judged to contain IISPs and the model-based component will continue to process it. Otherwise, it means the query is regardedas entity-only and the result of the rules is returned as the final output

s in real use cases of dialog systems, the queries in the shared task can be roughlydivided into two kinds, viz. queries with intent-indicating salient phrases and querieswithout. By intent-indicating salient phrase (IISP) it is meant a phrase in the query thatshows the intent of the query. E.g. the phrase “” in the query “” andthe phrases “” in the query “” are IISPs.

Spoken language understanding (SLU) comprises two tasks, intent identification andslot filling. That is, given the current query along with the previous queries in the samesession, an SLU system predicts the intent of the current query and also all slots (entitiesor labels) associated with the predicted intent. The significance of SLU lies in that eachtype of intent corresponds to a particular service API and the slots correspond to theparameters required by this API. SLU helps the dialog system to decide how to satisfythe user’s need by calling the right service with the right information

对话管理也可以看成是一个分类任务，即每个对话状态和一个合适的对话动作相对应．和其它有监督的学习任务一样，分类器可以从标注的语料库中训练得到．但是，在某状态下系统应该选择的动作不能仅仅是模仿在训练数据中同一状态对应的动作，而应该是选择合适的动作能够导致一个成功的对话．因此，把对话过程看成是一个决策过程更为合适，从而根据对话的整体成功来优化动作的选择过程［３２］．因而这是一个规划问题，并且可以用强化学习［３３］方法学习获得最优的结果

对话系统从本体构成和业务逻辑角度，可分为领域任务型和开放型的信息交互．领域任务型系统针对具体应用领域，具有比较清晰的业务语义单元的定义、本体结构以及用户目标范畴，例如航班查询、视频搜索、设备控制等等，这类交互往往是以完成特定的操作任务作为交互目标；而开放型信息交互则不针对特定领域，或说面向非常广泛的领域，交互目标并非业务任务，而是满足用户其它方面的需求，例如开放的百科问答、聊天等．它虽然能一定程度上显示人工智能的力量，但因其并不专注于帮助人解决现实任务问题，其实际使用范围较为狭窄．近年来，随着移动终端的高速发展，面向任务的自然人机对话系统和相关的认知控制理论得到了越来越多的学术和产业界重视，这也是本文讨论的重点

We com-plement recent work by showing the effec-tiveness of simple sequence-to-sequenceneural architectures with a copy mecha-nism. Our model outperforms more com-plex memory-augmented models by 7% inper-response generation and is on par withthe current state-of-the-art on DSTC2, areal-world task-oriented dialogue dataset

Both NLU and NLG are implementedwith template-based models

Symptom ExtractionWe follow the BIO(begin-in-out) schema for symptom identification(Figure 1). Each Chinese character is assigned alabel of ”B”, ”I” or ”O”. Also, each extractedsymptom expression is tagged withTrueorFalseindicating whether the patient suffers from thissymptom or not. In order to improve the anno-tation agreement between annotators, we createtwo guidelines for the self-report and the conver-sational data respectively. Each record is anno-tated by at least two annotators. Any inconsis-tency would be further judged by the third one.The Cohen’s kappa coefficient between two anno-tators are71%and67%for self-reports and con-versations respectively

In this paper, we make a move to builda dialogue system for automatic diagno-sis. We first build a dataset collected froman online medical forum by extractingsymptoms from both patients’ self-reportsand conversational data between patientsand doctors. Then we propose a task-oriented dialogue system framework tomake the diagnosis for patients automat-ically, which can converse with patients tocollect additional symptoms beyond theirself-reports. Experimental results on ourdataset show that additional symptoms ex-tracted from conversation can greatly im-prove the accuracy for disease identifica-tion and our dialogue system is able tocollect these symptoms automatically andmake a better diagnosis

Open-Domain Neural Dialogue Systems

the dataset used in our experiment hasonly the tags of filled information slots extracted by patternmatching between dialogue log and final order information

the agent model swaps the in-put and output sequences, and it also takes the tag of filledinformation slots as an input which is extracted from dia-logue in previous turns by pattern matching with the orderinformation in ground truth

By directly learningfrom the raw dialogue logs, the network takes the agent ut-Figure 2: The network structure: encoder-decoder structurewith the attention mechanismteranceXa:xa1;xa2;:::;xanas the input sequence and takescorresponding user utteranceYu:yu1;yu2;:::;yumas the tar-get sequence.

In the task-oriented dialogues, a user usually firstly showsthe intention to the agent and then answers the agent’s ques-tions one by one to specify the demand.

we propose a uSer andAgent Model IntegrAtion (SAMIA) framework inspired byan observation that the roles of the user and agent models areasymmetric. Firstly, this SAMIA framework model the usermodel as a Seq2Seq learning problem instead of ranking ordesigning rules. Then the built user model is used as a lever-age to train the agent model by deep reinforcement learning.In the test phase, the output of the agent model is filtered bythe user model to enhance the stability and robustness. Ex-periments on a real-world coffee ordering dataset verify theeffectiveness of the proposed SAMIA framework.

for example, comments and identifiers

If one object is part of another object, then we use a diamond at the start of the arrow (next to the containing object), and a normal arrow at the end.

Grid devices can be nested or layered along with other devices and your plug-ins,

ng meets human needs—and exchange value—value based on profit—Trimbur points to the often-contradictory relationship between the two forms of value that is realized w

Content has a core conditional quality, fluidity in terms of what shape it may take and where it may travel, and indeterminacy in terms of who may use it, to what ends, and how various uses may come to be valued.

a mutator method is a method used to control changes to a variable. They are also widely known as setter methods

class MyClassDef {

    public void setProperty(String propertyVal) { .. }

}

. Sensual objects exist for real objects, namely, me, or some other perceiver. So I’ve got the caricature of the table and the caricature of the chair, those caricatures have no relation to each other. They have relation only for me, because my experience unifies both of them. So the real is always the bridge for the two sensuals; the sensual is always the bridge for the two reals. And that’s what we try to analyse in Object-Oriented Philosophy

This principle is, I will show, a convenient fiction in this new work, enabling the philosopher to hear the call of things and to speak to and for them, despite the new rule that we cannot think of objects as being-for-us and must reject older philosophies smacking of "presence" and traditional ontology or ontotheology

Ontology is the philosophical study of existence. Object-oriented ontology (“OOO” for short) puts things at the center of this study. Its proponents contend that nothing has special status, but that everything exists equally–plumbers, cotton, bonobos, DVD players, and sandstone, for example. In contemporary thought, things are usually taken either as the aggregation of ever smaller bits (scientific naturalism) or as constructions of human behavior and society (social relativism). OOO steers a path between the two, drawing attention to things at all scales (from atoms to alpacas, bits to blinis), and pondering their nature and relations with one another as much with ourselves. (bogost.com)

challenge his friends

This principle is, I will show, a convenient fiction in this new work, enabling the philosopher to hear the call of things and to speak to and for them, despite the new rule that we cannot think of objects as being-for-us and must reject older philosophies smacking of “presence” and traditional ontology or ontotheology.

according to the new line of thinking, objects should be recognized for their indifference to us, for the sorts of things they do behind our backs, and for the ways in which they “are” behind appear-ances

colour as an object-centred