There is wide theoretical coverage of sophisticated voting despite a clear empirical gap. There are only a handful empirical examples of sophisticated voting which are documented. There is also no systematic searching approach to recover such cases. This paper offers the first systematic approach to identify votes where sophisticated voting seems likely. The algorithm developed here cannot stat...

Replication and majority voting are well-known and widely-used methods for achieving fault-tolerance in distributed systems. An open area of investigation is coordinating the voting in a secure manner, so as to withstand malicious attacks. The Timed-Buffer Distributed Voting Algorithm (TB-DVA), a secure distributed voting protocol, is introduced for this purpose. It is contrasted with several o...

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication co...

The recently created IETF 6TiSCH working group combines the high reliability and low-energy consumption of IEEE 802.15.4e Time Slotted Channel Hopping with IPv6 for industrial Internet of Things. We propose a distributed link scheduling algorithm, called Local Voting, for 6TiSCH networks that adapts the schedule to the network conditions. The algorithm tries to equalize the link load (defined a...

Many types of existing collections often contain repeating sequences which could be called as patterns. If these patterns are recognized they can be for instance used in data compression or for prediction. Extraction of these patterns from data collections with components generated in equidistant time and in finite number of levels is now a trivial task. The problem arises for data collections ...

This paper presented a voting principle based on multiple classifiers. This voting principle was based on the naïve Bayesian classification algorithm and a new method based on nearest to class kernel classifier that was proposed. The recognition ability of each classifier to each sample is not the same. A model of each classifier was obtained by the training on the train data, which acts as bas...

Replication and voting have been used extensively for providing fault tolerance in many types of systems. In this abstract, we briefly investigate their capability to improve accuracy, or precision, of results. We also discuss trade-offs between precision and fault tolerance degree that can be achieved through adjustment of replication degree and/or voting algorithm. These capabilities are illu...

UNLABELLED Finding motifs in many sequences is an important problem in computational biology, especially in identification of regulatory motifs in DNA sequences. Let c be a motif sequence. Given a set of sequences, each is planted with a mutated version of c at an unknown position, the motif finding problem is to find these planted motifs and the original c. In this paper, we study the VM model...