18595985
Fault-tolerant peer-to-peer search on small-world networks
This paper presents a small world architecture for P2P networks (SWAN) for content discovery in multi-group P2P systems. A semi-structured P2P algorithm of SWAN is utilized to create and find long-range shortcuts toward remote peer groups. In SWAN, not every peer node needs to be connected to remote groups, but every peer node can easily find which peer nodes have external connections to a specific peer group. From our analysis and simulation, SWAN has the advantages of both structured and unstructured P2P networks, and can achieve good performance in both stable and dynamic environments.
17142920
Service-Oriented Integration of Systems for Military Capability
Service oriented architecture (SOA) is becoming established in computing as a means to integrate processing and data across organisations. This paper proposes that system-level integration can benefit from service oriented architectural descriptions and loose coupling between the problem domain requirements and different system solutions. The problem domain is exemplified as military capability, from the UK Ministry of Defence (MoD), in particular, network enabled capability (NEC). Representations of military capability in the problem domain can be described in terms of processes. The processes are sequences of functions that can be described as services. Then different types of system solutions can implement the described services. Firstly, the paper presents an overview of conceptual SOA and in the context of military capability compares three levels of service integration: business services, systems services and computing services. Secondly, the paper presents a framework for evaluating the performance and effectiveness of service integration to compare different solutions in delivering military capability.
16432148
Delivering Sustainable Capability on Evolutionary Service-oriented Architecture
Network Enabled Capability (NEC) is the U.K. Ministry of Defence’s response to the quickly changing conflict environment in which its forces must operate. In NEC, systems need to be integrated in context, to assist in human activity and provide dependable inter-operation. In order to provide reliable and sustainable military capability, fast paced changes must be conducted without halting the operation of a capability. In this paper we present the concept of evolutionary service-oriented architecture for delivering sustainable capability. The reliability of the architecture is evaluated by simulations using a computer-based model. The simulation results indicate that the evolutionary service-oriented architecture can provide higher reliability and sustainability in the provision of capability in a dynamic environment.
16471179
Small World Architecture for Peer-to-Peer Networks
Small-world phenomenon has been observed in existing peer-to-peer (P2P) networks, such as Gnutella and Freenet. Due to the similarity of P2P networks to social networks, the previous small-world model proposed by Duncan Watts can be adopted in the design of P2P networks: each node is connected to some neighbouring nodes, and a group of nodes keep a small number of long links to randomly chosen distant nodes. Unfortunately, current unstructured search algorithms have difficulty distinguishing these random long-range shortcuts. This paper presents small world architecture for P2P networks (SWAN) with a semi-structured P2P search algorithm that is used to create and find long-range shortcuts toward remote peer groups. In SWAN, not every peer node needs to be connected to remote groups, but every peer node can easily find which peer nodes have external connections to a specific peer group
17479734
Self-Organization of Autonomous Peers with Human Strategies
Similarly to social networks where people are connected by their social relationships, two autonomous peer nodes can be connected in unstructured peer-to-peer (P2P) networks if users in those nodes are interested in each other's data. The similarity between P2P networks and social networks, where peer nodes are people and connections are relationships, leads us to believe that human strategies in social networks are useful for improving the performance of resource discovery by self-organising autonomous peers on unstructured P2P networks. In this paper, we present an efficient social-like peer-to-peer (ESLP) model for resource discovery by mimicking different human behaviours in social networks.
6983198
Investigation of Research towards Efficient Social Peer-to-Peer Networks
Peer-to-peer (P2P) networks attract attentions worldwide with their great success in file sharing networks (e.g. Napster, Gnutella, BitTorrent, and Kazaa). In the last decade, numerous studies have been devoted to the problem of resource discovery in P2P networks. However, efficient resource discovery remains a key challenge for large-scale P2P networks. An investigation of existing research on resource discovery towards efficient social P2P networks will be given in this paper.
14622249
Knowledge Inventory Management Using Actuarial Method
Knowledge inventory management is nowadays central issue as knowledge management becomes vital for organizations. Knowledge inventory management involves acquiring, retaining, deploying, idling, and abandoning technologies. Managers must make investment decisions about which technologies should be involved into inventory for future usage. Options-pricing model (OPM) has been widely accepted to make investment decisions. An alternative technology is regarded as an option to switch technologies to deal with future contingencies. This paper studies another kind of knowledge inventory problem which OPM is sort of inefficient to deal with. An organization might be forced to invest in some technology in the future because of some specific incident occurs, thus option is replaced by obligation. The characteristics of this kind of problem are portrayed and we show why OPM is not applicable. We treat the specific incident as an insurance accident to organizations and initial investment in some technology as insurance fee which can cover the loss when accident occurs. We use actuarial model to analyze the problem and give the evaluation methods in different conditions.
14971936
Does Familiarity Breed Content? Taking Account of Familiarity with a Topic in Personalizing Information Retrieval
We report on an evaluation of the effectiveness of considering a user's familiarity with a topic in improving information retrieval performance. This approach to personalization is based on previous results indicating differences in user search behavior and judgments according to his/her familiarity to the topic explored, and to research on using implicit sources of evidence to determine the user's context and preferences. Our attempt was to relate a topic-dependent concept and measure, familiarity with the topic, with topic-independent measures of documents such as readability, concreteness/abstractness, and specificity/generality. Contrary to our expectations, a user’s familiarity with a topic has no effect on the utility of readability or concrete/abstract scoring. We are encouraged, however, to find that high readability had a positive effect on search results, regardless of a user’s familiarity with a topic.
694781
A Decentralized Resource Discovery Based on Keywords Combinations and Node Clusters in Knowledge Grid
The organization and discovery of grid resources are foundational and key subjects in grid research. Many research works in this field have presented the solutions to the problem, but few of them are focused on knowledge resources. This paper aims to explore how to support user's knowledge requests submitted in form of multi-keywords. We present a decentralized resource discovery method based on keyword combinations and node clusters. In the method, hot keyword combinations are formed based on user's knowledge requests. Then, grid nodes can be clustered according to these keyword combinations and user's knowledge requests will be transmitted to those clusters that have high correlations with the requests.
13366067
Tarsal and Metatarsal Bone Mineral Density Measurement Using Volumetric Quantitative Computed Tomography
A new method for measuring bone mineral density (BMD) of the tarsal and metatarsals is described using volumetric quantitative computed tomography (VQCT) in subjects with diabetes mellitus and peripheral neuropathy. VQCT images of a single foot were acquired twice from eight subjects (mean age 51 [11 SD], seven males, one female). The cortical shells of the seven tarsal and five metatarsal bones were identified and semiautomatically segmented from adjacent bones. Volume and BMD of each bone were measured separately from the two acquired scans for each subject. Whole-bone semiautomatic segmentation measurement errors were determined as the root mean square coefficient of variation for the volume and BMD of 0.8% and 0.9%, respectively. In addition to the whole-bone segmentation methods, we performed atlas-based partitioning of subregions within the second metatarsal for all subjects, from which the volumes and BMDs were obtained for each subregion. The subregion measurement BMD errors (root mean square coefficient of variation) within the shaft, proximal end, and distal end were shown to vary by approximately 1% between the two scans of each subject. The new methods demonstrated large variations in BMDs between the 12 bones of the foot within a subject and between subjects, and between subregions within the second metatarsal. These methods can provide an important outcome measure for clinical research trials investigating the effects of interventions, aging, or disease progression on bone loss, or gain, in individual foot bones.
15609476
An Online Personalized Recommendation Model Based on Bayesian Networks
It is one of an important method of using Bayesian networks in electronic commercial recommended system. But the models of Bayesian networks for describing recommended system have a problem that it could not learn online. The paper puts forward an online personalized recommended model based on Bayesian networks. The paper uses a partial ordering to represent previous structure and find posterior distributions of every node on the orders to realize online structure learning. It also uses a correctional function to revise log likelihood for online parameter learning. The experiment shows that the model can be learned online for personalized recommended system.