Static Video Summarization through Optimum-Path Forest Clustering

This paper introduces the Optimum-Path Forest (OPF) classifier for static video summarization, being its results comparable to the ones obtained by some state-of-the-art video summarization techniques. The experimental section has been conducted using several image descriptors in two public datasets, followed by an analysis of OPF robustness regarding one ad-hoc parameter. Future works are guided to improve OPF effectiveness on each distinct video category.

Pseudechis guttatus venom proteome: Insights into evolution and toxin clustering

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Visuomotor processing and hand force coordination in dyslexic children during a visually guided manipulation task

Developmental Dyslexia negatively affects children's reading and writing ability and, in most cases, performance in sensorimotor tasks. These deficits have been associated with structural and functional alterations in the cerebellum and the posterior parietal cortex (PPC). Both neural structures are active during visually guided force control and in the coordination of load force (LF) and grip force (GF) during manipulation tasks. Surprisingly, both phenomena have not been investigated in dyslexic children. Therefore, the aim of this study was to compare dyslexic and non-dyslexic children regarding their visuomotor processing ability and GF-LF coordination during a static manipulation task. Thirteen dyslexic (8-14YO) and 13 age- and sex-matched non-dyslexic (control) children participated in the study. They were asked to grasp a fixed instrumented handle using the tip of all digits and pull the handle upward exerting isometric force to match a ramp-and-hold force profile displayed in a computer monitor. Task performance (i.e., visuomotor coordination) was assessed by RMSE calculated in both ramp and hold phases. GF-LF coordination was assessed by the ratio between GF and LF (GF/LF) calculated at both phases and the maximum value of a cross-correlation function (r(max)) and its respective time lag calculated at ramp phase. The results revealed that the RMSE at both phases was larger in dyslexic than in control children. However, we found that GF/LF, rmax, and time lags were similar between groups. Those findings indicate that dyslexic children have a mild deficit in visuomotor processing but preserved GF-LF coordination. Altogether, these findings suggested that dyslexic children could present mild structural and functional alterations in specific PPC or cerebellum areas that are directly related to visuomotor processing. (C) 2014 Elsevier Ltd. All rights reserved.

Multi-task control strategy for grid-tied inverters based on conservative power theory

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

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)

Metrics for Association Rule Clustering Assessment

Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.

Pre-task music improves swimming performance

The purpose of this study was to investigate the effects of pre--task music on swimming performance and other psychological variables. A randomized counterbalanced within--subjects (experimental and control condition) design was employed. Eighteen regional level male swimmers performed two 200m freestyle swimming time trials. Participants were exposed to either 5min of self--selected music (pre--task music condition) or 5min of silence (control condition) and, after 1 minute, performed the swimming task. Swimming time was significantly shorter (--1.44%) in the pre--task music condition. Listening to pre--task music increased motivation to perform the swimming task, while arousal remained unchanged. While fatigue increased after the swimming task in both conditions, vigour, ratings of perceived exertion and affective valence were unaltered. It is concluded, for the first time, that pre--task music improves swimming performance.

Task complexity reveals expertise of table tennis players

AIM: The purpose of this study was to examine the effect of intensive practice in table-­tennis on perceptual, decision-­making and motor-­systems. Groups of elite (HL=11), intermediate (LL=6) and control (CC=11) performed tasks of different levels. METHODS: All subjects underwent to reaction-­time-­test and response-­time-­test consisting of a pointing task to targets placed at distinct distances (15 and 25-­cm) on the right and left sides. The ball speed test in forehand and backhand condition just for HL and LL group. RESULTS: In CC group reaction time was higher compared to HL (P< 0.05) group. In the response-­time-­test, there was a significant main effect of distance (P< 0.0001) and the tennis-­table expertise (P= 0.011). In the ball speed test the HL were constantly faster compared to the LL in both forehand stroke (P< 0.0001) and backhand stroke (P< 0.0001). Overall, the forehand stroke was significantly faster than the backhand stroke. CONCLUSION: We can conclude that table-­tennis-­players have shorter response-­times than non-­athletes and the tasks of reaction-­time and response-­time are incapable to distinguish the performance of well-­trained table tennis players of the intermediate player, but the ball speed test seems be able to do it.

Scalable Web Server Clustering Technologies

The exponential growth of the Internet, coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. Server clustering has emerged as a promising technique to build scalable Web servers. In this article we examine the seminal work, early products, and a sample of contemporary commercial offerings in the field of transparent Web server clustering. We broadly classify transparent server clustering into three categories.

A Generic Autonomous Clustering-Based Heterogeneous Waveband Switching Architecture in WDM Networks

Heterogeneous waveband switching (HeteroWBS) in WDM networks reduces the network operational costs. We propose an autonomous clustering-based HeteroWBS architecture to support the design of efficient HeteroWBS algorithms under dynamic traffic requests in such a network.

Layered Clustering Communication Protocol for Wireless Sensor Networks

In this paper, we propose a Layered Clustering Hierarchy (LCH) communication protocol for Wireless Sensor Networks (WSNs). The design of LCH has two goals: scalability and energy-efficiency. In LCH, the sensor nodes are organized as a layered clustering structure. Each layer runs a distributed clustering protocol. By randomizing the rotation of cluster heads in each layer, the energy load is distributed evenly across sensors in the network. Our simulations show that LCH is effective in densely deployed sensor networks. On average, 70% of live sensor nodes are involved directly in the clustering communication hierarchy. Moreover, the simulations also show that the energy load and dead nodes are distributed evenly over the network. As studies prove that the performance of LCH depends mainly on the distributed clustering protocol, the location of cluster heads and cluster size are two critical factors in the design of LCH.

Regents Outreach and Economic Development Task Force Meeting

Let me begin today by thanking you, Regent Hassebrook, as Task Force Chair, and each of you who is a member of this Task Force, for focusing on outreach and economic development at the University as we work on behalf of Nebraska. We thank Regent Chair Howard Hawks for appointing this task force. The level of commitment this shows for the university's role in outreach and economic development is very important both to us in the university and to Nebraskans.

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.