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As power grids transition towards low-inertia net-works based on converter-interfaced renewable energy resources, they become increasingly vulnerable to extreme dynamics. Currently, the most advanced methods for signal processing in power systems are embed ...
This article reports on the current state of the OBI DICT project, a bilingual e-dictionary of oracle-bone inscriptions (OBI), incorporating artificial intelligence (AI) image recognition technology. It first provides a brief overview of the development of ...
In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional l ...
Lexicography has long faced the challenge of having too few specialists to document too many words in too many languages with too many linguistic features. Great dictionaries are invariably the product of many person-years of labor, whether the lifetime wo ...
Kamusi has been developing a system to analyze texts on the source side and present users with sense-specified dictionary options. Similarly to spellcheck, the user selects the intended meaning. We then use a multilingual lexical database to bridge to matc ...
Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...
Ieee2017
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This paper discusses Kamusi Pre:D, a system to improve translation by disambiguating word senses in a source document with reference to a large concept-based lexicon that is aligned by sense across numerous languages. Currently under active development, th ...
2016
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Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...
Springer2015
We study the problem of learning constitutive features for the effective representation of graph signals, which can be considered as observations collected on different graph topologies. We propose to learn graph atoms and build graph dictionaries that pro ...
In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...
