Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Interference competition and scavenging by Crematogaster ants (Hymenoptera: Formicidae) associated with the webs of the social spider Anelosimus eximius (Araneae: Theridiidae) in the central Amazon

Crematogaster cf. victima is a common inhabitant in the sheet web nests of the social spider Anelosimus eximius in the central Amazon basin near Manaus. A number of other ant species were found foraging on the non-sticky webs of A. eximius, but none of these reached the web occupation frequency found in C. cf. victima, nor, with the exception of an unidentified species of Pheidole, did they form satellite nests in the web, as did this species. Many prey which escaped the knock-down threads of the sheet web of A. eximius colonies were captured by ants in the lower web portions which they dominated. Furthermore, prey which were rejected by A. eximius, especially large, heavily sclerotized beetles, were also consumed by this ant. Repeated observations and experiments suggest that C. cf. victima is able to deter A. eximius activity through aerial venom release. Resources lost by A. eximius colonies to ants, especially C. cf. victima, in colonial web area and prey, may pose significant costs and may reduce colony growth.

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Seleção de características utilizando algoritmos evolucionistas e suas aplicações em reconhecimento de padrões

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

On the optical flow model selection through metaheuristics

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

Structure determination of hydroxytrypargine: A new tetrahydro-beta-carboline toxin from the venom of the spider Parawixia bistriata

A new, highly active tetrahydro-p-carboline toxin from the spider Parawixia bistriata, the most-common species of social spider occurring in Brazil, was isolated. The new toxin was identified as 1,2,3,4-tetrahydro-6-hydroxy-beta-carboline (= N-[3-(2,3,4,9-tetrahydro-6-hydroxy-1H-pyrido[3,4-b]indol-1-yl)propyl]guanidine; 3). This type of alkaloid, not common among spider toxins, was found to be the most-potent constituent of the spider's chemical weaponry to kill prey. When P bistriata catch arthropods in their web, they apparently attack their prey in groups of many individuals injecting their venoms. In vivo toxicity assays with 3 demonstrated a potent lethal effect to honeybees, giving rise to clear neurotoxic effects (paralysis) before death. The compound's toxicity (LD50 value) was determined to be ca. 8 ng/g of honeybee. The investigation of the pharmacological properties and neurotoxic actions of 3 may be used in the future for the development of new drugs to be applied for pest control in agriculture.

Dinâmica populacional e comportamento predatório individual da aranha social Parawixia bistriata (Rengger) (Araneae: Araneidae)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influence of Prey Size on the Capture of Social Wasps (Hymenoptera: Vespidae) by the Orb-Web Spider Nephilengys cruentata

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influence of Prey Size on the Capture of Social Wasps (Hymenoptera: Vespidae) by the Orb-Web Spider Nephilengys cruentata

The objective of the present study was to evaluate the plasticity of the hunting behavior of the spider Nephilengys cruentata (Araneae: Nephilidae) facing different species of social wasps. Considering that wasps can consume various species of spiders and that their poison can be used as defense against many predators, the effect of the corporal size of the prey was evaluated in the behavior of N. cruentata. Predation experiments were conducted using three species of social wasps of different sizes and the data registered in this research were compiled through annotations and filming of the hunting behavior of each spider, in relation to the offered prey. The results revealed that the size of the wasp and the sequential offer of prey change the hunting behavior of the spider, and prey of large size have high influence on this behavior.

Joint Optimization for Social Content Delivery in Wireless Networks

Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile, little is done to consider the impacts of the wireless environment, especially the capacity constraints and changing channel. In this dissertation, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver, when and how. We propose a scheduling framework that incorporates content-based reward and deliverability. Our approach utilizes the broadcast nature of wireless communication and social nature of content, by multicasting and precaching. Results indicate this novel joint optimization approach outperforms existing layered systems that separate recommendation and delivery, especially when the wireless network is operating at maximum capacity. Utilizing limited number of transmission modes, we significantly reduce the complexity of the optimization. We also introduce the design of a hybrid system to handle transmissions for both system recommended contents ('push') and active user requests ('pull'). Further, we extend the joint optimization framework to the wireless infrastructure with multiple base stations. The problem becomes much harder in that there are many more system configurations, including but not limited to power allocation and how resources are shared among the base stations ('out-of-band' in which base stations transmit with dedicated spectrum resources, thus no interference; and 'in-band' in which they share the spectrum and need to mitigate interference). We propose a scalable two-phase scheduling framework: 1) each base station obtains delivery decisions and resource allocation individually; 2) the system consolidates the decisions and allocations, reducing redundant transmissions. Additionally, if the social network applications could provide the predictions of how the social contents disseminate, the wireless networks could schedule the transmissions accordingly and significantly improve the dissemination performance by reducing the delivery delay. We propose a novel method utilizing: 1) hybrid systems to handle active disseminating requests; and 2) predictions of dissemination dynamics from the social network applications. This method could mitigate the performance degradation for content dissemination due to wireless delivery delay. Results indicate that our proposed system design is both efficient and easy to implement.

A survey: Particle Swarm Optimization based algorithms to solve premature convergence problem

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method based on the social behaviors of birds flocking or fish schooling. Although, PSO is represented in solving many well-known numerical test problems, but it suffers from the premature convergence. A number of basic variations have been developed due to solve the premature convergence problem and improve quality of solution founded by the PSO. This study presents a comprehensive survey of the various PSO-based algorithms. As part of this survey, the authors have included a classification of the approaches and they have identify the main features of each proposal. In the last part of the study, some of the topics within this field that are considered as promising areas of future research are listed.

Issues of Digital Ecosystems Approach to Biological Ecosystem Simulation Model Fit Optimization

This study examines the application of digital ecosystems concepts to a biological ecosystem simulation problem. The problem involves the use of a digital ecosystem agent to optimize the accuracy of a second digital ecosystem agent, the biological ecosystem simulation. The study also incorporates social ecosystems, with a technological solution design subsystem communicating with a science subsystem and simulation software developer subsystem to determine key characteristics of the biological ecosystem simulation. The findings show similarities between the issues involved in digital ecosystem collaboration and those occurring when digital ecosystems interact with biological ecosystems. The results also suggest that even precise semantic descriptions and comprehensive ontologies may be insufficient to describe agents in enough detail for use within digital ecosystems, and a number of solutions to this problem are proposed.

Cost effective campaigning in social networks

Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind. (C) 2016 Elsevier B.V. All rights reserved.

Spider monkey home ranges: A comparison of radio telemetry and direct observation

The ranging patterns of two male and five female spider monkeys (Ateles geoffroyi) were studied with the use of radio telemetry in Santa Rosa National Park, Costa Rica. The average size of a spider monkey home range was 62.4 hectares; however, range size varied with sex, and, for females, with the presence of a dependent infant. The probability of encountering a radio‐collared spider monkey in a three‐hour search using radio telemetry (0.91) was much greater than using a visual search (0.20), and telemetric data resulted in a larger estimate of mean home range size than did observational data, when all subjects were compared. However, the difference appeared to be owing to the presence of male ranges in the telemetric, but not the observational, data. When the size of home ranges derived from radio‐tracking data for adult females was compared to size of ranges for adult females derived from observations, the results were not significantly different. Adult males had larger home ranges than adult females, thus lending support to the hypothesis that males have adapted to the dispersion of females by occupying a large home range that overlaps the ranges of several adult females. The smallest home ranges were occupied by low‐weight females with dependent infants, perhaps reflecting social and energetic constraints. Copyright © 1988 Wiley‐Liss, Inc., A Wiley Company