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Muslim Marriage Goes Online: The Use of Internet Matchmaking by American Muslims

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Remember Me. While technological solutions have led to increased efficiency, online dating services have not been able to decrease the time needed to find a suitable match. Online dating users spend on average 12 hours a week online on dating activity [1]. Hinge, for example, found that only 1 in swipes on its platform led to an exchange of phone numbers [2]. Like Amazon and Netflix, online dating services have a plethora of data at their disposal that can be employed to identify suitable matches.

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MatchMaker FundRaising Software

Abstract Mobile social networks MSNs enable users to discover and interact with existing and potential friends both in the cyberspace and in the real world. Although mobile social network applications bring us much convenience, privacy concerns become the key security issue hindering their wide adoptions. In this paper, we propose a recommendation- based matchmaking scheme in multiple MSNs, which can help users to find their potential friends without disclosing their private information.

The correctness and security analyzing results show that our scheme can resist both semihonest and malicious attacks while providing matchmaking functionality against private data leakage.

between entities A and B is not “allocating A to B.” Rather, a match results in a mutual introduction of the two entities, who may activate a separate claim- ing.

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Trust-Aware and Fast Resource Matchmaking for Personalized Collaboration Cloud Service Abstract: In data-intensive cloud collaboration services with tens of thousands of users and million-level resources, means of providing personalized and trust-aware services quickly and simultaneously is a challenging issue. In this paper, we propose Per-trust, a trust-aware and fast resource matchmaking scheme for personalized QoS guaranteeing in collaboration cloud service. First, an integrated and trust-aware service broking architecture is proposed across the collaborative cloud computing environment; this architecture can provide trust computing and personalized resource matchmaking capacities.

Then, a resource clustering method is proposed based on the multidimensional properties of cloud resources; this method can accurately, quickly meet the personalized requirements of users. Finally, an innovative algorithm is proposed for the trust computing of service resources based on real-time and dynamic monitoring of data, thereby quickly and effectively providing trust-aware resource matchmaking. Different from existing methods, which focus only on QoS and trust issues, our approach adds a resource clustering step before QoS and trust evaluation.

Three key components are organically combined, namely, service broking architecture, resource clustering, and security and QoS-related trust computing.

Reaching High

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Federated distributed systems present new challenges to resource management. Conventional resource managers are based on a relatively static resource model and a centralized allocator that assigns resources to customers. Distributed environments, particularly those built to support high-throughput computing HTC , are often characterized by distributed management and distributed ownership.

Distributed management introduces resource heterogeneity: Not only the set of available resources, but even the set of resource types is constantly changing. Distributed ownership introduces policy heterogeneity: Each resource may have its own idiosyncratic allocation policy. To address these problems, we designed and implemented the Matchmaking resource management framework.

Customers and resources are all described by classified advertisements classads written in a simple but powerful formal language that describes their attributes and allocation policies. A Matchmaker server uses a policy-independent matching operation to discover pairings.