A collaborative citizen science platform for real-time volunteer computing and games

Volunteer computing (VC) or distributed computing projects are common in the citizen cyberscience (CCS) community and present extensive opportunities for scientists to make use of computing power donated by volunteers to undertake large-scale scientific computing tasks. Volunteer computing is generally a non-interactive process for those contributing computing resources to a project whereas volunteer thinking (VT) or distributed thinking, which allows volunteers to participate interactively in citizen cyberscience projects to solve human computation tasks. In this paper we describe the integration of three tools, the Virtual Atom Smasher (VAS) game developed by CERN, LiveQ, a job distribution middleware, and CitizenGrid, an online platform for hosting and providing computation to CCS projects. This integration demonstrates the combining of volunteer computing and volunteer thinking to help address the scientific and educational goals of games like VAS. The paper introduces the three tools and provides details of the integration process along with further potential usage scenarios for the resulting platform.Comment: 12 pages, 13 figure

Semantic Routed Network for Distributed Search Engines

Searching for textual information has become an important activity on the web. To satisfy the rising demand and user expectations, search systems should be fast, scalable and deliver relevant results. To decide which objects should be retrieved, search systems should compare holistic meanings of queries and text document objects, as perceived by humans. Existing techniques do not enable correct comparison of composite holistic meanings like: "evidences on role of DR2 gene in development of diabetes in Caucasian population", which is composed of multiple elementary meanings: "evidence", "DR2 gene", etc. Thus these techniques can not discern objects that have a common set of keywords but convey different meanings. Hence we need new methods to compare composite meanings for superior search quality. In distributed search engines, for scalability, speed and efficiency, index entries should be systematically distributed across multiple index-server nodes based on the meaning of the objects. Furthermore, queries should be selectively sent to those index nodes which have relevant entries. This requires an overlay Semantic Routed Network which will route messages, based on meaning. This network will consist of fast response networking appliances called semantic routers. These appliances need to: (a) carry out sophisticated meaning comparison computations at high speed; and (b) have the right kind of behavior to automatically organize an optimal index system. This dissertation presents the following artifacts that enable the above requirements: (1) An algebraic theory, a design of a data structure and related techniques to efficiently compare composite meanings. (2) Algorithms and accelerator architectures for high speed meaning comparisons inside semantic routers and index-server nodes. (3) An overlay network to deliver search queries to the index nodes based on meanings. (4) Algorithms to construct a self-organizing, distributed meaning based index system. The proposed techniques can compare composite meanings ~105 times faster than an equivalent software code and existing hardware designs. Whereas, the proposed index organization approach can lead to 33% savings in number of servers and power consumption in a model search engine having 700,000 servers. Therefore, using all these techniques, it is possible to design a Semantic Routed Network which has a potential to improve search results and response time, while saving resources

Use of latent semantic indexing for content based searching and routing of mobile agents on P2P network

The peer-to-peer (P2P) system has a number of nodes that are connected to each other in an unstructured or a structured overlay network. One of the most important problems in a P2P system is locating of resources that are shared by various nodes. Techniques such as Flooding and Distributed Hash-Table (DHT) have been proposed to locate resources shared by various nodes. Flooding suffers from saturation as number of nodes increase, while DHT cannot handle multiple keys to define and search a resource. Various further research works including multi agent systems (MAS) have been pursued that take unstructured or structured networks as a backbone and hence inherently suffer from problems. We present the solution that is more efficient and effective for discovering shared resources on a network that is influenced by content shared by nodes. Our solution presents use of multiple agents that manage the shared information on a node and a mobile agent called Reconnaissance Agent (RA) that is responsible for querying various nodes. To reduce the search load on nodes that have unrelated content, an efficient migration route is proposed for RA that is based on cosine similarity of content shared by nodes and user query. Results show reduction in search load and traffic due to communication, and increase in recall value for locating of resources defined by multiple keys using RA that are logically similar to user query. Furthermore, the results indicate that by use of our technique the relevance of search results is higher; that is obtained by minimal traffic generation/communication and hops made by RA

Hardware Architecture for Semantic Comparison

Semantic Routed Networks provide a superior infrastructure for complex search engines. In a Semantic Routed Network (SRN), the routers are the critical component and they perform semantic comparison as their key computation. As the amount of information available on the Internet grows, the speed and efficiency with which information can be retrieved to the user becomes important. Most current search engines scale to meet the growing demand by deploying large data centers with general purpose computers that consume many megawatts of power. Reducing the power consumption of these data centers while providing better performance, will help reduce the costs of operation significantly. Performing operations in parallel is a key optimization step for better performance on general purpose CPUs. Current techniques for parallelization include architectures that are multi-core and have multiple thread handling capabilities. These coarse grained approaches have considerable resource management overhead and provide only sub-linear speedup. This dissertation proposes techniques towards a highly parallel, power efficient architecture that performs semantic comparisons as its core activity. Hardware-centric parallel algorithms have been developed to populate the required data structures followed by computation of semantic similarity. The performance of the proposed design is further enhanced using a pipelined architecture. The proposed algorithms were also implemented on two contemporary platforms such as the Nvidia CUDA and an FPGA for performance comparison. In order to validate the designs, a semantic benchmark was also been created. It has been shown that a dedicated semantic comparator delivers significantly better performance compared to other platforms. Results show that the proposed hardware semantic comparison architecture delivers a speedup performance of up to 10^5 while reducing power consumption by 80% compared to traditional computing platforms. Future research directions including better power optimization, architecting the complete semantic router and using the semantic benchmark for SRN research are also discussed

Distributed coordination in unstructured intelligent agent societies

Current research on multi-agent coordination and distributed problem solving is still not robust or scalable enough to build large real-world collaborative agent societies because it relies on either centralised components with full knowledge of the domain or pre-defined social structures. Our approach allows overcoming these limitations by using a generic coordination framework for distributed problem solving on totally unstructured environments that enables each agent to decompose problems into sub-problems, identify those which it can solve and search for other agents to delegate the sub-problems for which it does not have the necessary knowledge or resources. Regarding the problem decomposition process, we have developed two distributed versions of the Graphplan planning algorithm. To allow an agent to discover other agents with the necessary skills for dealing with unsolved sub-problems, we have created two peer-to-peer search algorithms that build and maintain a semantic overlay network that connects agents relying on dependency relationships, which improves future searches. Our approach was evaluated using two different scenarios, which allowed us to conclude that it is efficient, scalable and robust, allowing the coordinated distributed solving of complex problems in unstructured environments without the unacceptable assumptions of alternative approaches developed thus far.As abordagens actuais de coordenação multi-agente e resolução distribuída de problemas não são suficientemente robustas ou escaláveis para criar sociedades de agentes colaborativos uma vez que assentam ou em componentes centralizados com total conhecimento do domínio ou em estruturas sociais pré-definidas. A nossa abordagem permite superar estas limitações através da utilização de um algoritmo genérico de coordenação de resolução distribuída de problemas em ambientes totalmente não estruturados, o qual permite a cada agente decompor problemas em sub-problemas, identificar aqueles que consegue resolver e procurar outros agentes a quem delegar os subproblemas para os quais não tem conhecimento suficiente. Para a decomposição de problemas, criámos duas versões distribuídas do algoritmo de planeamento Graphplan. Para procurar os agentes com as capacidades necessárias à resolução das partes não resolvidas do problema, criámos dois algoritmos de procura que constroem e mantêm uma camada de rede semântica que relaciona agentes dependentes com o fim de facilitar as procuras. A nossa abordagem foi avaliada em dois cenários diferentes, o que nos permitiu concluir que ´e uma abordagem eficiente, escalável e robusta, possibilitando a resolução distribuída e coordenada de problemas complexos em ambientes não estruturados sem os pressupostos inaceitáveis em que assentava o trabalho feito até agora

Models of Interaction as a Grounding for Peer to Peer Knowledge Sharing

Most current attempts to achieve reliable knowledge sharing on a large scale have relied on pre-engineering of content and supply services. This, like traditional knowledge engineering, does not by itself scale to large, open, peer to peer systems because the cost of being precise about the absolute semantics of services and their knowledge rises rapidly as more services participate. We describe how to break out of this deadlock by focusing on semantics related to interaction and using this to avoid dependency on a priori semantic agreement; instead making semantic commitments incrementally at run time. Our method is based on interaction models that are mobile in the sense that they may be transferred to other components, this being a mechanism for service composition and for coalition formation. By shifting the emphasis to interaction (the details of which may be hidden from users) we can obtain knowledge sharing of sufficient quality for sustainable communities of practice without the barrier of complex meta-data provision prior to community formation