Geographic Information Retrieval is concerned with retrieving documents in response to a spatially related query. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introduce multi-dimensional scattered ranking, where textually and spatially similar documents are ranked spread in the list, instead of consecutively. The effect of this is that doc...

We present some interesting properties related to the area compensation procedure to compare fuzzy numbers. It has been proved that this method produces more than a fuzzy interval order: it induces a ranking of fuzzy numbers. Some further results are given about the transitivity property and about computational aspects. Extensions to non-normal fuzzy numbers and fuzzy quantities are also proposed.

Automatic speech summarization is the task of generating a concise summary of a speech signal using a digital computer. The existing speech summarization systems rely on automatic speech recognition (ASR) transcripts and gold standard human summaries to generate summaries of speech signals. The limitations with these approaches are, ASR errors make summaries less usable by humans, also ASR syst...

The present report further investigates the multi-criteria decision making tool named Fuzzy Compromise Programming. Comparison of different fuzzy set ranking methods (required for processing fuzzy information) is performed. A complete sensitivity analysis concerning decision maker’s risk preferences was carried out for three water resources systems, and compromise solutions identified. Then, a ...

Recently, some researchers presented methods for ranking fuzzy numbers based on deviation degree. To avoid more applications that are possible or spread in the future, in this paper, we indicate that the proposed methods have drawbacks. Therefore, they cannot rank fuzzy numbers in all conditions.

We study the problem of ranking students by their abilities, solely based on responses to studentsourced multiple-choice questions. This addresses the crucial problem of scaling automatic assessment of students to very large class sizes. Current state-of-the-art methods (i) assume student responses obey a parameterized model, (ii) were designed for situations with trusted questions, and (iii) a...

An important component of a suitably automated machine learning process is the automation of the model selection which often contains some optimal selection of hyperparameters. The hyperparameter optimization process is often conducted with a black-box tool, but, because different tools may perform better in different circumstances, automating the machine learning workflow might involve choosin...

Feature selection is a crucial activity when knowledge discovery is applied to very large databases, as it reduces dimensionality and therefore the complexity of the problem. Its main objective is to eliminate attributes to obtain a computationally tractable problem, without affecting the quality of the solution. To perform feature selection, several methods have been proposed, some of them tes...

Sequence Models and Ranking Methods for Discourse Parsing A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Ben Wellner Many important aspects of natural language reside beyond the level of a single sentence or clause, at the level of the discourse, including: reference relations such anaphora, notions of topic/...