With the wide use of visual sensors in Internet Things (IoT) past decades, huge amounts images are captured people's daily lives, which poses challenges to traditional deep-learning-based image retrieval frameworks. Most such frameworks need a large amount annotated training data, expensive. Moreover, machines still lack human intelligence, as illustrated by fact that they pay less attention in...

Instance ranking problems intend to recover the ordering of instances in a data set with applications scientific, social and financial contexts. In this work, we concentrate on global robustness parametric instance terms breakdown point which measures fraction samples that need be perturbed order let estimator take unreasonable values. Existing notions do not cover so far. We propose define as ...

Cognitive psychology has uncovered two effects that have altered traditional views of human classification. Basic level effects suggest that humans prefer concepts at a particular level of generality, while typicality effects indicate that some instances of a class are more readily recognized as such than others. This paper describes a model of memory that accounts for basic level effects, typi...

We show that oblivious transfer of bits from A to B can be obtained from a single instance of the same primitive from B to A. Our reduction is perfect and shows that oblivious transfer is in fact a symmetric functionality. This solves an open problem posed by Crépeau and Sántha in 1991.

An application of relational instance-based learning to the complex task of expressive music performance is presented. We investigate to what extent a machine can automatically build ‘expressive profiles’ of famous pianists using only minimal performance information extracted from audio CD recordings by pianists and the printed score of the played music. It turns out that the machine-generated ...

We propose a generic, memory-based approach for the detection of implicit semantic roles. While state-of-the-art methods for this task combine hand-crafted rules with specialized and costly lexical resources, our models use large corpora with automated annotations for explicit semantic roles only to capture the distribution of predicates and their associated roles. We show that memory-based lea...

We present Quarantine, a system that enables datadriven selective isolation within concurrent server applications. Instead of constructing arbitrary isolation boundaries between components, Quarantine collects data to learn where such boundaries should be placed, and then instantiates said barriers to improve reliability. We present the case for data-driven selective isolation, and discuss the ...

This paper establishes a link between two supervised learning frameworks, namely multiple-instance learning (MIL) and learning from only positive and unlabelled examples (LOPU). MIL represents an object as a bag of instances. It is studied under the assumption that its instances are drawn from a mixture distribution of the concept and the non-concept. Based on this assumption, the classificatio...

While there has been a significant amount of theoretical and empirical research on the multiple-instance learning model, most of this research is for concept learning. However, for the important application area of drug discovery, a real-valued classification is preferable. In this paper we initiate a theoretical study of real-valued multiple-instance learning. We prove that the problem of find...