Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities

Adaptive behaviour of plants, including rapid changes in physiology, gene regulation and defence response, can be altered when linked to neighbouring plants by a mycorrhizal network (MN). Mechanisms underlying the behavioural changes include mycorrhizal fungal colonization by the MN or interplant communication via transfer of nutrients, defence signals or allelochemicals. We focus this review on our new findings in ectomycorrhizal ecosystems, and also review recent advances in arbuscular mycorrhizal systems. We have found that the behavioural changes in ectomycorrhizal plants depend on environmental cues, the identity of the plant neighbour and the characteristics of the MN. The hierarchical integration of this phenomenon with other biological networks at broader scales in forest ecosystems, and the consequences we have observed when it is interrupted, indicate that underground ‘tree talk’ is a foundational process in the complex adaptive nature of forest ecosystems.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.

An Optimum-Path Forest framework for intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conditional reasoning: A key to assessing computer-based knowledge-building communication processes

This article describes a methodological approach to conditional reasoning in online asynchronous learning environments such as Virtual-U VGroups, developed by SFU, BC, Canada, consistent with the notion of meaning implication: If part of a meaning C is embedded in B and a part of a meaning B is embedded in A, then A implies C in terms of meaning [Piaget 91]. A new transcript analysis technique was developed to assess the flows of conditional meaning implications and to identify the occurrence of hypotheses and connections among them in two human science graduate mixed-mode online courses offered in the summer/spring session of 1997 by SFU. Flows of conditional meaning implications were confronted with Virtual-U VGroups threads and results of the two courses were compared. Findings suggest that Virtual-U VGroups is a knowledge-building environment although the tree-like Virtual-U VGroups threads should be transformed into neuronal-like threads. Findings also suggest that formulating hypotheses together triggers a collaboratively problem-solving process that scaffolds knowledge-building in asynchronous learning environments: A pedagogical technique and an built-in tool for formulating hypotheses together are proposed. © Springer Pub. Co.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A New Cryptographic Scheme for Securing Dynamic Conferences in Data Networks

Dynamic conferencing refers to a scenario wherein any subset of users in a universe of users form a conference for sharing confidential information among themselves. The key distribution (KD) problem in dynamic conferencing is to compute a shared secret key for such a dynamically formed conference. In literature, the KD schemes for dynamic conferencing either are computationally unscalable or require communication among users, which is undesirable. The extended symmetric polynomial based dynamic conferencing scheme (ESPDCS) is one such KD scheme which has a high computational complexity that is universe size dependent. In this paper we present an enhancement to the ESPDCS scheme to develop a KD scheme called universe-independent SPDCS (UI-SPDCS) such that its complexity is independent of the universe size. However, the UI-SPDCS scheme does not scale with the conference size. We propose a relatively scalable KD scheme termed as DH-SPDCS that uses the UI-SPDCS scheme and the tree-based group Diffie- Hellman (TGDH) key exchange protocol. The proposed DH-SPDCS scheme provides a configurable trade-off between computation and communication complexity of the scheme.

Design of a Wireless Vision Sensor for Object Tracking in Wireless Vision Sensor Networks

The integration of CMOS cameras with embedded processors and wireless communication devices has enabled the development of distributed wireless vision systems. Wireless Vision Sensor Networks (WVSNs), which consist of wirelessly connected embedded systems with vision and sensing capabilities, provide wide variety of application areas that have not been possible to realize with the wall-powered vision systems with wired links or scalar-data based wireless sensor networks. In this paper, the design of a middleware for a wireless vision sensor node is presented for the realization of WVSNs. The implemented wireless vision sensor node is tested through a simple vision application to study and analyze its capabilities, and determine the challenges in distributed vision applications through a wireless network of low-power embedded devices. The results of this paper highlight the practical concerns for the development of efficient image processing and communication solutions for WVSNs and emphasize the need for cross-layer solutions that unify these two so-far-independent research areas.