Method framework

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  • he book begins by documenting the common challenges that must be addressed by system architecture engineering. It explores the major principles answering these challenges and forming the basis of MFESA. Next, the authors introduce MFESA, including its primary goals, inputs, tasks, outputs, and assumptions. Then they describe the fundamental concepts and terminology on which the systems architecture engineering is founded.

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  • I read the first edition of this book early in 1950, when I was nineteen. I thought then that it was by far the best book about investing ever written. I still think it is. To invest successfully over a lifetime does not require a stratospheric IQ, unusual business insights, or inside information. What’s needed is a sound intellectual framework for making decisions and the ability to keep emotions from corroding that framework. This book precisely and clearly prescribes the proper framework. You must supply the emotional discipline....

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  • Tạo ra một phương pháp chung Cũng như các lớp học định nghĩa chung chung, bạn cũng có thể sử dụng NET Framework. Để tạo ra phương pháp chung chung. Một phương pháp chung cho phép bạn chỉ định các tham số và kiểu trả về bằng cách sử dụng một tham số loại một cách tương tự như được sử dụng khi xác định một lớp học chung chung.

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  • FORM 185 Hidden fields When the above form object is serialized by {{=form}}, and because of the previous call to the accepts method, it now looks like this: 1 2 3 4 5 6 7 your name: Notice the presence of two hidden fields: " formkey" and " formname". Their presence is triggered by the call to accepts and they play two different and important roles: • The hidden field called " formkey" is a one-time token that web2py uses to prevent double submission of forms. The value of this key is generated when the form is serialized and stored in...

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  • his monograph presents methods for full comparative distributional analysis based on the relative distribution. This provides a general integrated framework for analysis, a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesis-driven summary measures, and the potential for decomposition - enabling the examination of complex hypotheses regarding the origins of distributional changes within and between groups.

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  • State-of-the-art statistical machine translation (MT) systems have made significant progress towards producing user-acceptable translation output. However, there is still no efficient way for MT systems to inform users which words are likely translated correctly and how confident it is about the whole sentence. We propose a novel framework to predict wordlevel and sentence-level MT errors with a large number of novel features. Experimental results show that the MT error prediction accuracy is increased from 69.1 to 72.2 in F-score. ...

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  • Creating a Generic Method As well as defining generic classes, you can also use the .NET Framework to create generic methods. A generic method allows you to specify parameters and return type by using a type parameter

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  • CONTENTS xi 6.14 7 Table Inheritance 179 181 182 185 186 186 187 188 189 189 193 194 195 197 198 198 200 200 201 202 203 210 211 212 213 214 215 216 217 218 220 220 223 225 227 228 Forms and Validators 7.1 FORM Hidden fields keepvalues onvalidation Forms and redirection Multiple forms per page No self-submission SQLFORM Insert/Update/Delete SQLFORM SQLFORM in HTML SQLFORM and uploads Storing the original filename Removing the action file Links to referencing records Prepopulating the form SQLFORM without database IO SQLFORM.

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  • TYPES 25 For user-defined classes, str and repr can be defined/redefined using the special operators str and repr . These are briefly described later on; for more, refer to the official Python documentation [38]. repr always has a default value. Another important characteristic of a Python string is that, like a list, it is an iterable object. 1 2 3 4 5 6 7 for i in 'hello': print i h e l l o list The main methods of a Python list are append, insert, and delete: 1 2 3 4 5 6 7 8 9 10 a = [1, 2, 3] print type(a)...

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  • CRUD 215 1 def data: return dict(form=crud()) would expose the following URLs: 1 2 3 4 5 http://.../[app]/[controller]/data/tables http://.../[app]/[controller]/data/create/[tablename] http://.../[app]/[controller]/data/read/[tablename]/[id] http://.../[app]/[controller]/data/delete/[tablename] http://.../[app]/[controller]/data/select/[tablename] However, the following action: 1 2 def create_tablename: return dict(form=crud.create(db.tablename)) would only expose the create method 1 http://...

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  • AUTHENTICATION 225 In this chapter, we are going to discuss different parts of RBAC one by one. 8.1 Authentication In order to use RBAC, users need to be identified. This means that they need to register (or be registered) and log in. Auth provides multiple login methods. The default one consists of identifying users based on the local auth user table. Alternatively, it can log in users against third-party basic authentication systems (for example a Twitter account), SMTP servers (for example Gmail), or LDAP (your corporate account).

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  • We have looked at confidentiality services, and also examined the information theoretic framework for security.

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  • Recognizing entailment at the lexical level is an important and commonly-addressed component in textual inference. Yet, this task has been mostly approached by simplified heuristic methods. This paper proposes an initial probabilistic modeling framework for lexical entailment, with suitable EM-based parameter estimation. Our model considers prominent entailment factors, including differences in lexical-resources reliability and the impacts of transitivity and multiple evidence.

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  • We propose a framework for generating an abstractive summary from a semantic model of a multimodal document. We discuss the type of model required, the means by which it can be constructed, how the content of the model is rated and selected, and the method of realizing novel sentences for the summary.

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  • In text categorization, feature selection (FS) is a strategy that aims at making text classifiers more efficient and accurate. However, when dealing with a new task, it is still difficult to quickly select a suitable one from various FS methods provided by many previous studies. In this paper, we propose a theoretic framework of FS methods based on two basic measurements: frequency measurement and ratio measurement. Then six popular FS methods are in detail discussed under this framework.

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  • Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classification task. ...

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  • In recent years, research in natural language processing has increasingly focused on normalizing SMS messages. Different well-defined approaches have been proposed, but the problem remains far from being solved: best systems achieve a 11% Word Error Rate. This paper presents a method that shares similarities with both spell checking and machine translation approaches. The normalization part of the system is entirely based on models trained from a corpus. Evaluated in French by 10-fold-cross validation, the system achieves a 9.3% Word Error Rate and a 0.83 BLEU score. ...

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  • We define the problem of recognizing entailed relations – given an open set of relations, find all occurrences of the relations of interest in a given document set – and pose it as a challenge to scalable information extraction and retrieval. Existing approaches to relation recognition do not address well problems with an open set of relations and a need for high recall: supervised methods are not easily scaled, while unsupervised and semi-supervised methods address a limited aspect of the problem, as they are restricted to frequent, explicit, highly localized patterns.

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  • Maximum entropy (Maxent) is useful in many areas. Iterative scaling (IS) methods are one of the most popular approaches to solve Maxent. With many variants of IS methods, it is difficult to understand them and see the differences. In this paper, we create a general and unified framework for IS methods. This framework also connects IS and coordinate descent (CD) methods. Besides, we develop a CD method for Maxent. Results show that it is faster than existing iterative scaling methods1 .

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  • Extraction of relations between entities is an important part of Information Extraction on free text. Previous methods are mostly based on statistical correlation and dependency relations between entities. This paper re-examines the problem at the multiresolution layers of phrase, clause and sentence using dependency and discourse relations. Our multi-resolution framework ARE (Anchor and Relation) uses clausal relations in 2 ways: 1) to filter noisy dependency paths; and 2) to increase reliability of dependency path extraction.

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