Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the data. Popular approaches to such learning include (and often combine) frequency-based approaches and statistical analysis. However, the quality of results is often far from satisfactory. Though most previous investig...

Outsourcing computation to remote parties (“workers”) is an increasingly common practice, owing in part to the growth of cloud computing. However, outsourcing raises concerns that outsourced tasks may be completed incorrectly, whether by accident or because workers cheat to minimize their cost and optimize their gain. The goal of this paper is to explore, using game theory, the conditions under...

With the advent and growing use of crowdsourcing labor markets for a variety of applications, optimizing the quality of results produced is of prime importance. The quality of the results produced is typically a function of the performance of crowd workers. In this paper, we investigate the notion of treating crowd workers as ‘learners’ in a novel learning environment. This learning context is ...

Estimating query performance is the task of predicting the quality of results returned by a search engine in response to a query. In this paper, we focus on pre-retrieval prediction methods for the medical domain. We propose a novel predictor that exploits a thesaurus to ascertain how difficult queries are. In our experiments, we show that our predictor outperforms the state-of-the-art methods ...

This paper presents a new computationally adaptive formulation of the full-search block-matching (FSBM) motion estimation. A key feature of our approach is to progressively adjust the number of computations required per block to the picture variation by applying a data-driven thresholding. Unlike the related methods, the proposed algorithm utilizes a simple threshold updating mechanism which ne...

The problem of retrieving time series similar to a specified query pattern has been recently addressed within the Case Based Reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue would be of paramount importance in medical domains, where many patient parameters are often collected in the form of time series. In this paper, we describe a novel framework...

The aim of our research is to produce and assess short summaries to aid users’ relevance judgements, for example for a search engine result page. In this paper we present our new metric for measuring summary quality based on representativeness and judgeability, and compare the summary quality of our system to that of Google. We discuss the basis for constructing our evaluation methodology in co...

Skyline operator has gained much attention in the last decade and is proved to be valuable for multi-criteria decision making. This paper presents a novel Flexible Interactive Querying (FlexIQ) framework for user feedback-based Select-Project-Join (SPJ) query refinement in databases. In FlexIQ, the user feedback is used to discover the query intent. In addition, we have used the skyline operato...

In an era that, searching the WWW for information becomes a tedious task, it is obvious that mainly search engines and other data mining mechanisms need to be enhanced with characteristics such as NLP in order to better analyze and recognize user queries and fetch data. We present an efficient mechanism for stemming and tagging for the Greek language. Our system is constructed in such a way tha...