Requirements of a middleware for managing a large, heterogeneous programmable network

Programmable networking is an increasingly popular area of research in both industry and academia. Although most programmable network research projects seem to focus on the router architecture rather than on issues relating to the management of programmable networks, there are numerous research groups that have incorporated management middleware into the programmable network router software. However, none seem to be concerned with the effective management of a large heterogeneous programmable network. The requirements of such a middleware are outlined in this paper. There are a number of fundamental middleware principles that are addressed in this paper; these include management paradigms, configuration delivery, scalability and transactions. Security, fault tolerance and usability are also examined—although these are not essential parts of the middleware, they must be addressed if the programmable network management middleware is to be accepted by industry and adopted by other research projects

Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Visual analytics in FCA-based clustering

Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary Proceedings of the 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201