Vitaliy Vitsentiy

Bio: Vitaliy Vitsentiy is an academic researcher. The author has contributed to research in topics: Spiking neural network & Spike train. The author has an hindex of 1, co-authored 2 publications receiving 2 citations.

A Ring-Type Digital Spiking Neural Network and Spike-Train Approximation

Abstract: This paper studies ring-type digital spiking neural networks that can exhibit multi-phase synchronization phenomena of various periodic spike-trains. First, in order to realize approximation of a class of spike-trains, a winner-take-all switching is applied to the network. Second, in order to design efficient networks, relationship between approximation error and the network size is investigated. Executing Verilog simulation, approximation function is confirmed experimentally.

Adaptive dual control of topic-based information retrieval

Abstract: Information Retrieval is an important albeit imperfect component of information technologies. A problem of insufficient diversity of retrieved documents is one of the primary issues studied in this research. This study shows that this problem leads to a decrease of precision and recall, traditional measures of information retrieval effectiveness. This thesis presents an adaptive IR system based on the theory of adaptive dual control. The aim of the approach is the optimization of retrieval precision after all feedback has been issued. This is done by increasing the diversity of retrieved documents. This study shows that the value of recall reflects this diversity. The Probability Ranking Principle is viewed in the literature as the “bedrock” of current probabilistic Information Retrieval theory. Neither the proposed approach nor other methods of diversification of retrieved documents from the literature conform to this principle. This study shows by counterexample that the Probability Ranking Principle does not in general lead to optimal precision in a search session with feedback (for which it may not have been designed but is actively used). Retrieval precision of the search session should be optimized with a multistage stochastic programming model to accomplish the aim. However, such models are computationally intractable. Therefore, approximate linear multistage stochastic programming models are derived in this study, where the multistage improvement of the probability distribution is modelled using the proposed feedback correctness method. The proposed optimization models are based on several assumptions, starting with the assumption that Information Retrieval is conducted in units of topics. The use of clusters is the primary reasons why a new method of probability estimation is proposed. The adaptive dual control of topic-based IR system was evaluated in a series of experiments conducted on the Reuters, Wikipedia and TREC collections of documents. The Wikipedia experiment revealed that the dual control feedback mechanism improves precision and S-recall when all the underlying assumptions are satisfied. In the TREC experiment, this feedback mechanism was compared to a state-of-the-art adaptive IR system based on BM-25 term weighting and the Rocchio relevance feedback algorithm. The baseline system exhibited better effectiveness than the cluster-based optimization model of ADTIR. The main reason for this was insufficient quality of the generated clusters in the TREC collection that violated the underlying assumption.

A User Interface of Relevance Feedback for Interactive Information Retrieval Systems

Abstract: A dual interactive information retrieval (DIIR) system takes into account not only relevance of documents, but also possible effects of the retrieved documents on user feedback when deciding which documents should be retrieved thus optimizing the search session. Such optimization can improve quality of information retrieval, especially under uncertainty and long information search. Since these methods assume availability of a feedback, organization of the documents in topics and iterative information search process, then new user interfaces need to be developed for possibility of use of the new methods. A user interface for DIIR system is proposed in the paper, after a description of DIIR.