star identification is one of the most important stages in attitude determination with star trackers. this can be performed using matching algorithms between observed stars and a master star catalogue. the main challenge in this approach is to provide a fast and reliable identification algorithm that is sufficiently robust in different pointing views of the star tracker optical system in the sp...

Weighted voting games are ubiquitous mathematical models which are used in economics, political science, neuroscience, threshold logic, reliability theory and distributed systems. They model situations where agents with variable voting weight vote in favour of or against a decision. A coalition of agents is winning if and only if the sum of weights of the coalition exceeds or equals a specified...

Ensemble methods are learning algorithms that classify new data points by synthesizing the predictions of a set of classifiers. Many methods for constructing ensembles have been proposed such as weighted voting, manipulations of training samples, features, or labels. The paper proposes a novel ensemble algorithm which constructs ensembles by manipulating the label set given to the learning algo...

We present three results on the complexity of Minimax Approval Voting. First, we study Minimax Approval Voting parameterized by the Hamming distance d from the solution to the votes. We show Minimax Approval Voting admits no algorithm running in time O(2 log ), unless the Exponential Time Hypothesis (ETH) fails. This means that the O(d) algorithm of Misra et al. [AAMAS 2015] is essentially opti...

Although voting methods are a viable way to improve classification algorithm performance, these have usually been applied to complete training datasets. We propose a new voting methodology which is based on the success of each individual classifier as it is applied to particular classes in a training dataset. We test some specific variations on this theme and have found as much as a 12.8% impro...

This paper presents a new class of synchronous deterministic non-authenticated algorithms for reaching interactive consistency (Byzantine agreement). The algorithms are based on voting and error-correcting codes and require considerably less data communication than the original algorithm, whereas the number of rounds and the number of modules meet the minimum bounds. These algorithm based on vo...

Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. W...

Weighted voting games are ubiquitous mathematical models which are used in economics, political science, neuroscience, threshold logic, reliability theory and distributed systems. They model situations where agents with variable voting weight vote in favour of or against a decision. A coalition of agents is winning if and only if the sum of weights of the coalition exceeds or equals a specified...

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information ex...

In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as "atlases"). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken f...