This paper presents a novel adaptive resolution technique for audio time stretching. The signal's transience is analyzed in both time and frequency, and controls the time-frequency resolution of the processing. Higher time resolution is used for transients and higher frequency resolution for steady sounds. To preserve phase coherence between components of different transience, only the magnitude is processed adaptively. This is done by first using a single resolution phase vocoder, and then by applying a multiresolution magnitude correction step. The proposed technique uses a decomposition of the signal in both time and frequency according to several degrees of transience. Yet the actual time stretching transformation is entirely based on the phase vocoder.

Wireless sensor networks consist of a large number of distributed sensor devices, which are connected and coordinated through multi‐hop routing. Due to the existence of correlated information and redundancy in measuring data, data messages can be combined and merged by performing data aggregation function in the routing process. To reduce energy consumption is a major optimisation objective of data aggregation approaches, which can be achieved by decreasing the compulsory communication load of routing. From the theoretical level, an energy model is proposed to validate the benefits of data aggregation on energy consumption. The key parameters which may impact the aggregation performance are further discussed. Finally, the corresponding simulations are implemented in order to verify the analysis conclusions from the theoretical model, and the comparison results can effectively reflect the advantages of data aggregation in different scenarios.

As a typical data aggregation technique in wireless sensor networks, the spanning tree has the ability of reducing the data redundancy and therefore decreasing the energy consumption. However, the tree construction normally ignores some other practical application requirements, such as network lifetime, convergence time and communication interference. In this case, the way how to design a tree structure subjected to multi-objectives becomes a crucial task, which is called as multi-objective steiner tree problem (MOSTP). In view of this kind of situation, a multi-objective optimization framework is proposed, and a heuristic algorithm based on jump particle swarm optimization (JPSO) with a specific double layer encoding scheme is introduced to discover Pareto optimal solution. Furthermore, the simulation results validate the feasibility and high efficiency of the novel approach by comparison with other approaches.

The widespread availability of personal mobile devices, combined with the increasing availability of stationary public devices such as large interactive displays, creates new opportunities for computer-supported collaborative work. In particular, these two factors enable the emergence of collaborative scenarios, whether planned or spontaneous, in any location, and previous obstacles to such collaborative settings such as limitations on the number of devices available for use and infrastructure costs can be overcome more easily. As hardware restrictions diminish, the need for software toolkits that simplify the development of distributed collaborative applications allowing for device heterogeneity, true multi-user interaction and spontaneous emergence increases. In this article, we describe the Toolkit for Web-based Interactive Collaborative Environments whose aim is to address these issues. This is done using current standard web technologies extended for real-time application (and structured using specific development guidelines) while ensuring compatibility with the manifold new evolutions in the currently ongoing development of open web platform (HTML5, websockets, etc). While our own work has mainly focused on synchronous co-located collaborative systems (same place/same time), our solution, the technologies used, as well as the concepts that are introduced are easily extendable for remote and/or asynchronous collaboration.

Interaction with multiple mouse pointers is becoming widespread for collocated collaboration on shared displays, but most technologies used to implement it do not support telepresent users. While web technologies are a common standard for applications that enable telepresent collaboration, they do not support the multipointer interaction needed for collaboration in collocated settings. We propose an approach to provide multipointer interaction in web applications by adding multipointer support to the web browser and addressing pointer handling in a JavaScript framework.

When comparing digital board games with their traditional counterparts, it becomes clear that certain features such as graphics, mundane task automation or saving and restoring the state of the game have been greatly improved.Nonetheless, the transition to a digital environment leads to a loss of the flexibility that makes traditional board games inherently popular. While modifying aspects of the game is straight forward in traditional board games, achieving such a level of customization in the digital domain requires deep knowledge of and access to the game source code. In this paper we focus on board games and by means of an in-depth online survey we validate our previous observation, namely that enhancements should be made to digital board games by incorporating gaming facets found in the physical environment,e.g. support for flexibility by means of house rules. To this end,we introduce a conceptual model for the design of digital board games, which is supported by a set of visual programming tools to enable game development according to the principles set out by our proposed model. The set of the tools along with the underlying intuitive model comprise the FLEXIBLERULES framework, which enables and facilitates flexible and extensible game design and development.

Much work has been done to exploit the effectiveness and efficiency of job scheduling upon distributed computational--resources. With regard to existing resource topology and administrative constraints, scheduling approaches are designed for different hierarchic layers, for example, scheduling for job queues of local resource management systems (local scheduling), and scheduling for job queues of high level schedulers (also known as meta-schedulers or grid schedulers). Such scheduling approaches mainly focus on optimizing job queues of the hosting nodes, which are interconnected with computational resources directly or indirectly. In the real world (or in a community-based grid), a grid is comprised of nodes with different computing power and scheduling preferences, which in turn, raise a notable opportunity that is to exploit and optimize the process of job sharing between reachable grid nodes via improving the job allocation and efficiency ratio. In our work, we introduce a novel scheduling protocol which dedicates to disseminate scheduling events happened on each involving node to as many candidate nodes as possible. By means of the proposed protocol, scheduling process of each received job consists of several phases with awareness of grid volatility, and dynamic scheduling and rescheduling is allowed as long as the job execution has not started yet. To this end, a set of concerning algorithms and processing steps are described. A prototype of our scheduling approach is being implemented within the SmartGRID project.

Critical to the successful deployment of grid systems is their ability to guarantee efficient meta-scheduling, namely optimal allocation of jobs across a pool of sites with diverse local scheduling policies. The centralized nature of current meta-scheduling solutions is not well suited for the envisioned increasing scale and dynamicity of next-generation grids, the success of which relies on the development of fully distributed, flexible and autonomic systems tailored to very large sets of highly volatile and heterogeneous resources. In this respect, we introduce a fully distributed grid meta-scheduling scheme that effectively addresses the concerns regarding the scalability and adaptability of future grid systems. Our approach employs a lightweight protocol, called A RiA, that is based on peer-to-peer communication between grid nodes, and makes use of dynamic rescheduling to consider and adapt to changes in the availability of resources. Extensive evaluation by means of an in depth simulation study highlighted the effectiveness of the proposed solution in improving the overall performance in terms of job completion time and load-balancing.

Many current Evolutionary Algorithms suffer from a tendency to prematurely lose their capability to incorporate new genetic material, resulting in a stagnation in suboptimal points. To successfully apply these methods on increasingly complex problems, the ability to generate useful variations leading to continuous improvements is vital. Nevertheless, there is a major difficulty in finding computational extensions to the evolutionary paradigm that ensures a continuous emergence of new qualitative solutions, as the essence of the Darwinian paradigm - the natural selection - acts as a stabilizing force, keeping the population into an evolutionary equilibria.

Although all three conventional c-means clustering algorithms, namely hard c-means (HCM), fuzzy c-means (FCM), and possibilistic c-means (PCM), had their merits in the development of clustering theory, none of them are generally good solutions for unsupervised classification. Several hybrid solutions have been proposed to produce mixture algorithms. Possibilistic-fuzzy hybrids generally attempt to get rid of the FCM's sensitivity to outliers and PCM's coincident cluster prototypes, while hard-fuzzy mixtures usually aim at quicker convergence while preserving FCM's accurate partitions. This paper presents a unifying approach to c-means clustering: the novel clustering model is considered as a linear combination of the FCM, PCM, and HCM objective functions. The optimal solution is obtained via evolutionary computation. Our main goal is to reveal the properties of such mixtures and to formulate some rules that yield accurate partitions.

The intrinsic feature of Estimation of Distribution Algorithms lies in their ability to learn and employ probabilistic models over the input spaces. Discovery of the appropriate model usually implies a computationally expensive comprehensive search, where many models are proposed and evaluated in order to find the best value of some model discriminative scoring metric.

Efficient, scalable and robust grid service provisioning is one of the cornerstones of next generation grid systems. In agreement with this vision, we propose a flexible grid service provisioning framework built on a two layer architecture. A lower layer provides membership management and basic communication upon a self-organized peer-to-peer overlay maintained and optimized using a fully distributed bio-inspired algorithm. An upper layer exploits these facilities to deliver high-level grid services such as resource discovery and monitoring. We evaluate the proposed solution on different scenarios and assess its qualities concerning performance, reliability, fault-resilience and network communication efficiency.

This paper proposes a new approach for modelling, testing and prototyping pervasive, possibly mobile, and distributed applications. It describes a set of tools aimed at supporting designers in the conceptualisation of their application and in the software development stage, and proposes a method for checking the validity of their design. The article also presents a pervasive application implemented and evaluated using our approach. It concludes with propositions for improvements in order to build a complete modelling, prototyping and testing framework for pervasive applications.

This paper describes a distributed algorithm to construct and maintain a peer-to-peer network overlay with bounded diameter. The proposed approach merges a bio-inspired self-organized behavior with a pure peer-to-peer approach, in order to adapt the overlay to underlying changes in the network topology. Ant colonies are used to collect and spread information across all peers, whereas pheromone trails help detecting crashed nodes. Construction of the network favors balanced distribution of links across all peers, so that the resulting topology does not exhibit large hubs. Fault resilience and recovery mechanisms have also been implemented to prevent network partition in the event of node crashes. Validation has been conducted through simulations of different network scenarios.

One of the most important decisions that influence the performance of evolutionary algorithms is the way individuals are selected for recombination. Two new selection operators that explore more promising regions of the search space are proposed in order to avoid the search becoming trapped into a local optimum. The first operator is a variant of the proportional selection and the second a variant of the tournament selection - both of them using information about the best ancestor of each individual within the population. In order to prove the efficiency of the proposed operators, several instances of the travelling salesman problem are considered. Experimental results show an acceleration of the search process when using the proposed selection schemes, compared to the most popular existing selection operators. While the first operator performs better only in the first stages of the algorithm, the second outperforms the other selection operators in all its stages.

A new evolutionary algorithm for problems having potential solutions encoded as permutations is proposed. The introduced algorithm is based on the collaboration between individuals that exchange information in order to accelerate the search process. Numerical experiments prove the efficiency of the proposed technique. Categories and Subject Descriptors

Network resources management issues in complex and dynamic scenarios require decentralized solutions and adaptive systems to face critical and unattended situations. Bio-inspired techniques such as swarm intelligence algorithms, have proved to be robust and suitable for managing tasks like routing, load-balancing or resource discovery. In this paper we describe Solenopsis, a framework for the development, simulation and deployment of ant-algorithms, which is aimed at supporting network management middlewares. The system provides a modular and scalable environment that can be distributed over a network. Ants are coded using a simple programming language, and are able to migrate across nodes. Two basic load-balancing algorithms are presented and evaluated, as an example of how this tool works and can be used in practice.

This paper presents a new locomotion interface that provides users with the ability to engage in a life-like walking experience using stepping in place. Stepping actions are performed on top of a flat platform that has an embedded grid of switch sensors that detect footfalls pressure. Based on data received from sensors, the system can compute different variables that represent user's walking behavior such as walking direction, walking speed, standstill, jump, and walking. The overall platform status is scanned at a rate of 100Hz with which we can deliver real-time visual feedback reaction to user actions. The proposed system is portable and easy to integrate into major virtual environment with large projection feature such as CAVE and DOME systems. The overall weight of the Walking-Pad is less than 5 Kg and can be connected to any computer via USB port, which make it even controllable via a portable computer.

The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.