Map matching is the problem of matching recorded geographic coordinates to a logical model of the real world. A GPS navigation device sends coordinates by an error between 2 and 10 meters. This causes confusion about the true location of the vehicle especially in dense urban areas. Matching a vehicle’s location to the true road segment is a challenge.
As the programs represented as dataflow graphs can be implemented in the hardware level, a performance gain is achieved compared to ordinary programs, especially in the execution of data retrieval queries. The purpose of this project is to create a frontend application and necessary framework for generation of the dataflow graph from an SQL query.
Much of the content on the Web is not globally accessible. Barriers to access go far beyond access to Internet and devices. Disabilities, such as vision impairment are also important factors, for which W3C (World Wide Web Consortium) have been developing standards for technology providers - such as Web Browsers. However, accessibility of content goes beyond these factors as well. Language, literacy, specialization, culture, etc. are all related to whether content is truly accessible. For example, legal documents, even if made available are difficult to comprehend by ordinary people.
Machine Learning methods have long been applied to financial data. We explore the correlation (or the lack thereof) of financial data, such as the BIST 100 index or individual stock prices, to features extracted from financial news and past data. We intend to use the advancements in text classification and Natural Language Processing capabilities in general, and machine learning models to extract the features of Turkish news documents and stock price series using statistical methods and to analyze the correlation of the extracted features with the financial data.
An information retrieval system which provides structural and comprehensive classification of species in documents and articles is crucial in bioinformatics studies. Having this information spread through scientific articles and web pages leads to a need for automatically detecting bacteria entities in text, semantically tagging them using taxonomy, and finally extracting the classification among them. These are the challenges set forth by the Bacteria Biotopes Task of the BioNLP Shared Task 2016. This paper describes a system for bacteria entity normalization through the NCBI taxonomy.
