A Finite State and Data-Oriented Method for Grapheme to Phoneme Conversion

A finite-state method, based on leftmost longest-match replacement, is presented for segmenting words into graphemes, and for converting graphemes into phonemes. A small set of hand-crafted conversion rules for Dutch achieves a phoneme accuracy of over 93%. The accuracy of the system is further improved by using transformation-based learning. The phoneme accuracy of the best system (using a large set of rule templates and a `lazy' variant of Brill's algoritm), trained on only 40K words, reaches 99% accuracy.Comment: 8 page

A Cost-based Optimizer for Gradient Descent Optimization

As the use of machine learning (ML) permeates into diverse application domains, there is an urgent need to support a declarative framework for ML. Ideally, a user will specify an ML task in a high-level and easy-to-use language and the framework will invoke the appropriate algorithms and system configurations to execute it. An important observation towards designing such a framework is that many ML tasks can be expressed as mathematical optimization problems, which take a specific form. Furthermore, these optimization problems can be efficiently solved using variations of the gradient descent (GD) algorithm. Thus, to decouple a user specification of an ML task from its execution, a key component is a GD optimizer. We propose a cost-based GD optimizer that selects the best GD plan for a given ML task. To build our optimizer, we introduce a set of abstract operators for expressing GD algorithms and propose a novel approach to estimate the number of iterations a GD algorithm requires to converge. Extensive experiments on real and synthetic datasets show that our optimizer not only chooses the best GD plan but also allows for optimizations that achieve orders of magnitude performance speed-up.Comment: Accepted at SIGMOD 201

Lazy User Behaviour

In this position paper we suggest that a user will most often choose the solution (device) that will fulfill her (information) needs with the least effort. We call this “lazy user behavior”. We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user’s detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. The context of this paper is the use of mobile devices and mobile services. We present the lazy user theory of solution selection, two case examples, and discuss the implications of lazy user behavior on user attachment to mobile services and devices, and to planning and execution of mobile services.User Attachment; Lazy User; Mobile Services; Mobile Devices; Adoption; Acceptance; Least Effort

Implementasi Pembelajaran di Era Revolusi Industri 4.0 di SMP Negeri 2 Lubuklinggau

The development of education is undergoing a rapid, dynamic transformation with technological advances in the era of the industrial revolution 4.0. This change is felt by the teaching and learning process at SMP Negeri 2 Lubuklinggau implementing 4.0-based learning in its application that has not been maximized and the existing technology is not used as well as possible. This study uses a qualitative approach with this type of research being field research, interview data collection techniques, observation and documentation. Analysis technique according to Miles and Humbersman. The findings of the first research, the implementation of PAI learning at SMP Negeri 2 Lubuklinggau still uses conventional methods, complete learning resources, delivering appropriate and clear material. Second, the efforts of teachers in the era of the industrial revolution 4.0 to master learning technology by using smartphones for online learning. Third, the supporters are facilities and infrastructure, internet network, focus, and learning resources. The obstacle is that students feel pampered by technology that is instant and easy to access, students are lazy to read in detail

Memory-Based Lexical Acquisition and Processing

Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described.Comment: 18 page