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We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5-6 years. We show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. We also present AI technologies, implemented in table and algorithm search, that are special search modes in CiteSeerX. While it is challenging to rebuild a system like Cite-SeerX from scratch, many of these AI technologies are transferable to other digital libraries and search engines.

The rapidly developing field of diagrammatic knowledge representation and reasoning is surveyed. The origins and rationale of the field, basic principles and methodologies, as well as selected applications are discussed. Closely related areas, like visual languages, data presentation, and visualization are briefly introduced as well. Basic sources of material for further study are indicated.

We describe a variety of projects developed as part of a course in Artificial Intelligence at the University of Minnesota. The projects cover navigation of small mobile robots and learning to accomplish simple tasks, and require a variety of approaches from neural networks to genetic programming to reactive behaviors. The projects have all been implemented on real robots. We discuss how the combination of robotics with Artificial Intelligence adds value to the learning of AI concepts and how the fun of building and programming a robot is a highly motivating force for the learning process. 1 Introduction The major goal of this paper is to describe examples of integration of real robotics projects in a course in Artificial Intelligence. The examples presented here are some of the class projects done by students taking a course in Artificial Intelligence at the University of Minnesota.

We have a procedure for using this learning set to form a predictor '(x; L) --- if the input is x we ...

Running the code is as easy as install Docker, make dev, and run a script. Open an issue if you want to discuss something to do, or email me. By default, DeepOSM will analyze about 200 sq. For training data, DeepOSM cuts tiles out of NAIP images, which provide 1-meter-per-pixel resolution, with RGB infrared data bands. For training labels, DeepOSM uses PBF extracts of OSM data, which contain features/ways in binary format that can be munged with Python.

Since then, the project has been directed by C. Lee Giles. While it is challenging to rebuild a system like Cite-SeerX from scratch, many of these AI technologies are transferable to other digital libraries and search engines. This is different from arXiv, Harvard ADS, and machine cluster to a private cloud using virtualization PubMed, where papers are submitted by authors or techniques (Wu et al. 2014). CiteSeerX extensively pushed by publishers. Unlike Google Scholar and leverages open source software, which significantly Microsoft Academic Search, where a significant portion reduces development effort. Red Hat of documents have only metadata (such as titles, Enterprise Linux (RHEL) 5 and 6 are the operating authors, and abstracts) available, users have full-text systems for all servers. Tomcat 7 is CiteSeerX keeps its own repository, which used for web service deployment on web and indexing serves cached versions of papers even if their previous servers. MySQL is used as the database management links are not alive any more. In additional to system to store metadata. Apache Solr is used paper downloads, CiteSeerX provides automatically for the index, and the Spring framework is used in extracted metadata and citation context, which the web application. In this section, we highlight four AI solutions that are Document metadata download service is not available leveraged by CiteSeerX and that tackle different challenges from Google Scholar and only recently available in metadata extraction and ingestion modules from Microsoft Academic Search. Finally, CiteSeerX (tagged by C, E, D, and A in figure 1).

CiteSeerX is a digital library search engine that provides access to more than 4 million academic documents with nearly a million users and millions of hits per day. Artificial intelligence (AI) technologies are used in many components of CiteSeerX, e.g. to accurately extract metadata, intelligently crawl the web, and ingest documents. We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.