Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

An intelligent configuration-sampling based local motion planner for robotic manipulators

Manipulator motion planning is a task which relies heavily on the construction of a configuration space prior to path planning. However when fast real-time motion is needed, the full construction of the manipulator's high-dimensional configu-ration space can be too slow and expensive. Alternative planning methods, which avoid this full construction of the manipulator's configuration space are needed to solve this problem. Here, one such existing local planning method for manipulators based on configuration-sampling and subgoal-selection has been extended. Using a modified Artificial Potential Fields (APF) function, goal-configuration sampling and a novel subgoal selection method, it provides faster, more optimal paths than the previously proposed work. Simulation results show a decrease in both runtime and path lengths, along with a decrease in unexpected local minimum and crashing issues.

Directional Modulation Transmitter Synthesis using Particle Swarm Optimization

Phased DM transmitter array synthesis using particle swarm optimization (PSO) is presented in this paper. The PSO algorithm is described in details with key parameters provided for 1-D four-element half-wavelength spaced QPSK DM array synthesis. A DM transmitter array for boresight and 30º direction secure communications are taken as examples to validate the proposed synthesis approach. The optimization process exhibits good convergence performance and solution quality.

A Hybrid Swarm Based Approach in Addressing University Timetabling Problems

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter's examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

A novel hybrid swarm based approach for curriculum based course timetabling problem

This work applies a hybrid approach in solving the university curriculum-based course timetabling problem as presented as part of the 2nd International Timetabling Competition 2007 (ITC2007). The core of the hybrid approach is based on an artificial bee colony algorithm. Past methods have applied artificial bee colony algorithms to university timetabling problems with high degrees of success. Nevertheless, there exist inefficiencies in the associated search abilities in term of exploration and exploitation. To improve the search abilities, this work introduces a hybrid approach entitled nelder-mead great deluge artificial bee colony algorithm (NMGD-ABC) where it combined additional positive elements of particle swarm optimization and great deluge algorithm. In addition, nelder-mead local search is incorporated into the great deluge algorithm to further enhance the performance of the resulting method. The proposed method is tested on curriculum-based course timetabling as presented in the ITC2007. Experimental results reveal that the proposed method is capable of producing competitive results as compared with the other approaches described in literature

The perihelion activity of comet 67P/Churyumov-Gerasimenko as seen by robotic telescopes

Around the time of its perihelion passage the observability of 67P/Churyumov-Gerasimenko from Earth was limited to very short windows each morning from any given site, due to the low solar elongation of the comet. The peak in the comet's activity was therefore difficult to observe with conventionally scheduled telescopes, but was possible where service/queue scheduled mode was possible, and with robotic telescopes. We describe the robotic observations that allowed us to measure the total activity of the comet around perihelion, via photometry (dust) and spectroscopy (gas), and compare these results with the measurements at this time by Rosetta's instruments. The peak of activity occurred approximately two weeks after perihelion. The total brightness (dust) largely followed the predictions from Snodgrass et al. 2013, with no significant change in total activity levels from previous apparitions. The CN gas production rate matched previous orbits near perihelion, but appeared to be relatively low later in the year.

Status of SuperWASP I (La Palma)

SuperWASP is an ultra-wide field (over 300 sq. degrees) photometric survey project designed to monitor stars between 7 - 15 mag to high precision and with high cadence over long (greater than or equal to2 months) timescales. The primary science goal of this project is the detection of exoplanetary transits, as well as NEOs and optical transients. The resulting photometric catalogue will be made public via a web-based interface. The SuperWASP instrument consists of an array of cameras each with a 7.8degrees x 7.8degrees field of view, guided by a robotic fork mount and sited in a fibreglass enclosure at the Observatorio de Roque de los Muchachos (ORM), La Palma, Canary Islands. In this progress report, we describe the specifications of the instrument, its semi-automated operation and pipeline data reduction.

Training-induced changes in the pattern of triceps to biceps activation during reaching tasks after chronic and severe stroke

This exploratory study was undertaken to investigate the mechanisms that contributed to improvements in upper limb function following a novel training program. Surface electromyography (EMG) was used to examine training-induced changes in the pattern of triceps and biceps activation during reaching tasks in stroke survivors with severe paresis in the chronic stage of recovery. The EMG data were obtained in the context of a single blind randomised clinical trial conducted with 42 stroke survivors with minimal upper limb muscle activity and who were more than 6 months post-stroke. Of the 33 participants who completed the study, 10 received training of reaching using a non-robotic upper limb training device, the SMART Arm, with EMG triggered functional electrical stimulation (EMG-stim), 13 received training of reaching using the SMART Arm alone, and 10 received no intervention. Each intervention group engaged in 12 1-h training sessions over a 4-week period. Clinical and laboratory measures of upper limb function were administered prior to training (0 weeks), at completion (4 weeks) and 2 months (12 weeks) after training. The primary outcome measure was 'upper arm function' which is Item 6 of the Motor Assessment Scale (MAS). Laboratory measures consisted of two multijoint reaching tasks to assess 'maximum isometric force' and 'maximum distance reached'. Surface EMG was used to monitor triceps brachii and biceps brachii during the two reaching tasks. To provide a comparison with normal values, seven healthy adults were tested on one of the reaching tasks according to the same procedure. Study findings demonstrated a statistically significant improvement in upper limb function for stroke participants in the two training groups compared to those who received no training however no difference was found between the two training groups. For the reaching tasks, all stroke participants, when compared to normal healthy adults, exhibited lower triceps and biceps activation and a lower ratio of triceps to biceps activation. Following training, stroke participants demonstrated increased triceps activation and an increased ratio of triceps to biceps activation for the task that was trained. Better performance was associated with greater triceps activation and a higher ratio of triceps to biceps activation. The findings suggest that increased activation of triceps as an agonist and an improved coordination between triceps and biceps could have mediated the observed changes in arm function. The changes in EMG activity were small relative to the changes in arm function indicating that factors, such as the contribution of other muscles of reaching, may also be implicated.

Artificial gravity reveals that economy of action determines the stability of sensorimotor coordination.

Background
When we move along in time with a piece of music, we synchronise the downward phase of our gesture with the beat. While it is easy to demonstrate this tendency, there is considerable debate as to its neural origins. It may have a structural basis, whereby the gravitational field acts as an orientation reference that biases the formulation of motor commands. Alternatively, it may be functional, and related to the economy with which motion assisted by gravity can be generated by the motor system.
Methodology/Principal Findings
We used a robotic system to generate a mathematical model of the gravitational forces acting upon the hand, and then to reverse the effect of gravity, and invert the weight of the limb. In these circumstances, patterns of coordination in which the upward phase of rhythmic hand movements coincided with the beat of a metronome were more stable than those in which downward movements were made on the beat. When a normal gravitational force was present, movements made down-on-the-beat were more stable than those made up-on-the-beat.
Conclusions/Significance
The ubiquitous tendency to make a downward movement on a musical beat arises not from the perception of gravity, but as a result of the economy of action that derives from its exploitation.