The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Intelligent search for distributed information sources using heterogeneous neural networks

As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heteroge-neous neural networks can be used in the design of an intelligent distributed in-formation retrieval (DIR) system. In particular, three typical neural network models - Kohoren's SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources

Sequence to Sequence Learning for Query Expansion

Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansion training set using sentence-embeddings-based Keyword Extraction. We therefore assessed the ability of the Sequence to Sequence neural networks to capture expanding relations in the words embeddings' space.Comment: 8 pages, 2 figures, AAAI-19 Student Abstract and Poster Progra

Information Retrieval Models

Many applications that handle information on the internet would be completely\ud inadequate without the support of information retrieval technology. How would\ud we find information on the world wide web if there were no web search engines?\ud How would we manage our email without spam filtering? Much of the development\ud of information retrieval technology, such as web search engines and spam\ud filters, requires a combination of experimentation and theory. Experimentation\ud and rigorous empirical testing are needed to keep up with increasing volumes of\ud web pages and emails. Furthermore, experimentation and constant adaptation\ud of technology is needed in practice to counteract the effects of people that deliberately\ud try to manipulate the technology, such as email spammers. However,\ud if experimentation is not guided by theory, engineering becomes trial and error.\ud New problems and challenges for information retrieval come up constantly.\ud They cannot possibly be solved by trial and error alone. So, what is the theory\ud of information retrieval?\ud There is not one convincing answer to this question. There are many theories,\ud here called formal models, and each model is helpful for the development of\ud some information retrieval tools, but not so helpful for the development others.\ud In order to understand information retrieval, it is essential to learn about these\ud retrieval models. In this chapter, some of the most important retrieval models\ud are gathered and explained in a tutorial style

Modelling Dependency Structures Produced by the Introduction of a Flipped Classroom

Teaching processes have been changing in the lasts few decades from a traditional lecture-example-homework format to more active strategies to engage the students in the learning process. One of the most popular methodologies is the flipped classroom, where traditional structure of the course is turned over by moving out of the classroom, most basic knowledge acquisition. However, due to the workload involved in this kind of methodology, an objective analysis of the results should be carried out to assess whether the lecturer’s workload is worth the effort or not. In this paper, we compare the results obtained from two different methodologies: traditional lecturing and flipped classroom methodology, in terms of some performance indicators and an attitudinal survey, in an introductory statistics course for engineering students. Finally, we analysed the changes in the relationships among variables of interest when the traditional teaching was moved to a flipped classroom by using Bayesian networks