I Love Indonesia

A brief introduction to Dart : Dart is a brand new language for client and server side web development from Google. The language provides class-based object-orientation and allows developing modular and structured applications. For client side development Google learned from Javascript and provides an own high featured library for DOM (Document Object Model1 ) manipulation and event handling. This paper will introduce client side development with Dart by developing a small sample application. Allowing server side development, too, Dart allows to create homogeneous systems covering both client and server. As conclusion Dart shows potential for challenging modern web development. The one question remaining for client-side development using Dart is the following: Will the browsers implement native support for Dart – will Dart challenge a growing community for Javascript? Dart (first called Dash) is a class-based, object-oriented programming language for the web developed by Google and pu

There are five types of learning problem that have received the preponderance of attention in machine learning. The first four are all cases of function estimation, grouped along two dimensions: whether the learning task is supervised or unsupervised, and whether the variable to be predicted is nominal or real-valued. Classification involves supervised learning of a function f (x) whose value is nominal, that is, drawn from a finite set of possible values. The learned function is called a classifier. It is given instances x of one or another class, and it must determine which class each instance belongs to; the value f (x) is the classifier’s prediction regarding the class of the instance. For example, an instance might be a particular word in context, and the classification task is to determine its part of speech. The learner is given labeled data consisting of a collection of instances along with the correct answer, that is, the correct class label, for each instance. The unsupervise

Introduction of Semi-supervised Learning for Computational Linguistic Creating sufficient labeled data can be very time-consuming. Obtaining the output sequences is not difficult: English texts are available in great quantity. What is time-consum Subsequent work in computational linguistics led to development of alternative algorithms for semisupervised learning, the algorithm of Yarowsky being a prominent example. These algorithms were developed specifically for the sorts of problems that arise frequently in computational linguistics: problems in which there is a linguistically correct answer, and large amounts of unlabeled data, but very little labeled data. Unlike in the example of acoustic modeling, classic unsupervised learning is inappropriate, because not just any way of assigning classes will do. The learning method is largely unsupervised, because most of the data is unlabeled, but the labeled data is indispensable, because it provides the only characterization of the linguist

Hidden Markov Models in Supervised and unsupervised training The probabilistic models used by Church and DeRose in the papers just cited were Hidden Markov Models (HMMs), imported from the speech recognition community. An HMM describes a probabilistic process or automaton that generates sequences of states and parallel sequences of output symbols. Commonly, a sequence of output symbols represents a sentence of English or of some other natural language. An HMM, or any model, that defines probabilities of word sequences (that is, sentences) of a natural language is known as a language model. The probabilistic automaton defined by an HMM may be in some number of distinct states. The automaton begins by choosing a state at random. Then it chooses a symbol to emit, the choice being sensitive to the state. Next it chooses a new state, emits a symbol from that state, and the process repeats. Each choice is stochastic – that is, probabilistic. At each step, the automaton makes its choice at ra

Computational linguistics : Probabilistic methods Computational linguistics seeks to describe methods for natural language processing, that is, for processing human languages by automatic means. Since the advent of electronic computers in the late 1940s, human language processing has been an area of active research; machine translation in particular attracted early interest. Indeed, the inspiration for computing machines was the creation of a thinking automaton, a machina sapiens, and language is perhaps the most distinctively human cognitive capacity. In early work on artificial intelligence, there was something of a competition between discrete, “symbolic” reasoning and stochastic systems, particularly neural nets. But the indispensability of a firm probabilistic basis for dealing with uncertainty was soon recognized. In computational linguistics, by contrast, the presumption of the sufficiency of grammatical and logical constraints, supplemented perhaps by ad hoc heuristics, was muc

Harmoni Cinta Harmoni Cinta is an album by Gita Gutawa. It was released in 2009 by Sony Music Indonesia, with a part of the sales used to send poor students to school. Produced over a period of nine months, it was a collaboration between Gutawa and numerous Indonesian musicians, including her father Erwin, Melly Goeslaw, and Glenn Fredly. Production of Harmoni Cinta required nine months, from June 2008 to March 2009. It involved numerous Indonesian musicians, including Gita Gutawa's father Erwin, as well Glenn Fredly, Yovie Widyanto, and Melly Goeslaw. Singaporean songwriter Dick Lee also contributed the song "Remember", while "Aku Cinta Dia", a cover of the title song of Chrisye's album Aku Cinta Dia, was also included. The vocals were recorded in Aluna Studio, Jakarta and the City of Prague Philharmonic Orchestra and Sofia Symphonic Orchestra recorded their pieces in their respective cities. Six of the songs were mixed at 301 Studio in Sydney, while th