Abstract: The Quake-Catcher Network (QCN) project uses the low cost sensors, i.e., accelerometers attached to volunteers' computers, to detect earthquakes. The master-worker topology currently used in QCN and other similar projects suffers from major weaknesses. The centralized master can fail to collect data if the volunteers' computers are not connected to the network, or it can introduce significant delays in the warning if the network is congested. We propose to solve these problems by using multiple servers in a more advanced network topology than the simple master-worker configuration. We first consider several critical scenarios in which the current master-worker configuration of QCN can hinder the early warning of an earthquake, and then integrate the advanced network topology around multiple servers and emulate these critical scenarios in a simulation environment to quantify the benefits and costs of our proposed solution. We show how our solution can reduce the time to detect an earthquake from 1.8 s to 173 ms in case of network congestion and the number of lost trickle messages from 2,013 to 391 messages in case of network failure.

Abstract: The indices currently used by scholarly databases, such as Google scholar, to rank scientists, do not attach weights to the citations. Neither is the underlying network structure of citations considered in computing these metrics. This results in scientists cited by well-recognized journals not being rewarded, and may lead to potential misuse if documents are created purely to cite others. In this paper we introduce a new ranking metric, the p-index (pagerank-index), which is computed from the underlying citation network of papers, and uses the pagerank algorithm in its computation. The index is a percentile score, and can potentially be implemented in public databases such as Google scholar, and can be applied at many levels of abstraction. We demonstrate that the metric aids in fairer ranking of scientists compared to h-index and its variants. We do this by simulating a realistic model of the evolution of citation and collaboration networks in a particular field, and comparing h-index and p-index of scientists under a number of scenarios. Our results show that the p-index is immune to author behaviors that can result in artificially bloated h-index values.

Abstract: Link prediction is an important task in social network analysis, which also has applications in other domains like, recommender systems, molecular biology and criminal investigations. The classical methods of link prediction are based on graph topology structure and path features but few consider clustering information. The cluster in graphs is densely connected group of vertices and sparsely connected to other groups. Actually, the clustering results contain the essential information for link prediction, and these vertices common neighbors may play different roles depending on if they belong to the same cluster. Based on this assumption and characteristics of the common social networks, in this paper, we propose a link prediction method based on clustering and global information. Our experiments on both synthetic and real-world networks show that this method can improve link prediction accuracy as the number of cluster grows.

Abstract: The article presents a technology for dynamic knowledge-based building of Domain-Specific Languages (DSL) for description of data-intensive scientific discovery tasks using BigData technology. The proposed technology supports high level abstract definition of analytic and simulation parts of the task as well as integration into the composite scientific solutions. Automatic translation of the abstract task definition enables seamless integration of various data sources within single solution.

Abstract: We simulate and analyze dynamical phase transitions in a Boolean neural network with initial random connections. Since we treat a stochastic evolution by using a noise intensity, we show from our condition that there exists a critical value for the noise intensity. The nature of the phase transition are found numerically and analytically in two connections (of probability density function) and one random network.