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Mariana Damova
Mozaika
Sofia, Bulgaria
 

Mariana Damova is the CEO of Mozaika, a company providing research and solutions for data science, natural interfaces, and human insight. Mozaika specializes in building semantic information infrastructures in different verticals, such as business information delivery, human resources management, cultural heritage, earth observation, and water management.

Her background is in natural language processing, semantic web technologies, and artificial intelligence (AI), with significant academic and industry experience in Europe and North America.

She has taught graduate courses, conducted research, and led international interdisciplinary projects and teams on virtual customer service, speech-to-speech machine translation, search based on linguistic principles, voice-enabled emotion recognition, an expert system supporting child protective services, water resources management, content mapping, and historical archives.

She has collaborated with many different universities and organizations, including The National Archives of the UK, the British Museum, the Historical Archives of the European Commission, the European Space Agency, Europeana, and the Bulgarian Academy of Sciences.

Mariana holds a PhD from the University of Stuttgart and a mini-MBA from McGill University, and currently teaches a graduate course at New Bulgarian University.

She has been a reviewer for Computing Reviews since 2008, and has authored more than 50 publications (books and papers) related to linguistics and semantic technologies.

Mariana enjoys museums, theater, and reading.


     

Neuromemrisitive architecture of HTM with on-device learning and neurogenesis
Zyarah A., Kudithipudi D.  ACM Journal on Emerging Technologies in Computing Systems 15(3): 1-24, 2019. Type: Article

This very comprehensive technical account presents a thoroughly worked out “architecture for the spatial pooler (SP)” of the hierarchical temporal memory (HTM) algorithm. This algorithm is designed to produce invariant representations ...

 

An improved algorithm for solving scheduling problems by combining generative adversarial network with evolutionary algorithms
Chen M., Yu R., Xu S., Luo Y., Yu Z.  CSAE 2019 (Proceedings of the 3rd International Conference on Computer Science and Application Engineering, Sanya, China,  Oct 22-24, 2019) 1-7, 2019. Type: Proceedings

This paper discusses the optimization of results derived from evolutionary algorithms by augmenting them with generative adversarial nets (GAN). The proposed research presents a hybrid algorithm that combines GAN with a genetic algorithm (GA). GAN...

 

Indonesian automatic text summarization based on a new clustering method in sentence level
Cai Z., Lin N., Ma C., Jiang S.  BDE 2019 (Proceedings of the 2019 International Conference on Big Data Engineering, Hong Kong, Hong Kong,  Jun 11-13, 2019) 30-35, 2019. Type: Proceedings

A sentence-centered account of text summarization, this work can be applied to any language. Characteristics of the Indonesian language are irrelevant to the proposed approach and are not discussed. Neither is the suitability of the proposed appro...

 

Neural network classifiers using a hardware-based approximate activation function with a hybrid stochastic multiplier
Li B., Qin Y., Yuan B., Lilja D.  ACM Journal on Emerging Technologies in Computing Systems 15(1): 1-21, 2019. Type: Article

Li et al. present a novel approach for optimizing neural network implementations, that is, “a new architecture of stochastic neural networks” with a hidden approximate activation function and a hybrid stochastic multiplier that substan...

 

 How testing helps to diagnose proof failures
Petiot G., Kosmatov N., Botella B., Giorgetti A., Julliand J.  Formal Aspects of Computing 30(6): 629-657, 2018. Type: Article

Petiot et al. present testing software components that help optimize the effort of “applying deductive verification to formally prove that a [computer] program respects its formal specification.”...

 
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