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Conflict resolution models play key roles in coordinating simultaneous interactions in multi-user collaborative virtual environments (VEs). Currently, conflict resolution models are first-come-first-serve (FCFS) and dynamic priority (DP). Known to be unfair, the FCFS model grants all interaction opportunities to the agilest user. Instead, the DP model permits all users the perception of equality in...
Multi-user usability of collaborative virtual environments (VEs) require the consideration of users' socio-human needs. However, most investigations of the usability have focused on either the technologies involved or individual user experience. Few have examined the effect of interaction models on multi-user usability, by taking account the socio-human needs. Thus, we have undertaken a study on such...
There is a growing interest in socio-technical networks that encompass people, process and technology. They capture connections among technical artifacts and human resources. Existing studies have shown that social networks among resources that collaborate to work on a business process instance have an impact on the performance of the instance. Hence, the social network among the resources should...
Collaboration Engineering (CE) is an approach to designing collaborative work systems for high-value tasks, and transferring them to practitioners to execute for themselves without support from a collaboration expert. The stakes are high on a CE project, so it would be useful to have a way to evaluate the performance of the collaboration engineers (CEs) who design the work systems. One can evaluate...
In this paper we evaluate the interplay between scholarly social networking and academic output. To this end, we tested the hypotheses that the activity of users on scholarly social networks is associated with academic output, and that the intra-or interdisciplinary background of scholars affects academic output and online activity. The quantitative data used for this study was collected from the...
Aggregate answer ratings serve as a metric of collective intelligence in social Q&A communities. The patterns by which participants in a social Q&A community rate and recommend answers are analyzed through the lens of first-mover advantage, to address the question of whether the first answer posted has a ratings advantage over those subsequently submitted. As part of a long-term participant...
the present study utilizes social computing techniques to enhance the content-based recommender systems. Coined as Enhanced Content-based Algorithm using Social Networking (ECSN), this recommender algorithm is applied in academic social networks to suggest the most relevant items to members of these online societies. In addition to considering user's own preferences, ECSN takes advantage of the interest...
The advent of proliferation of social networking is high on use in present era. A co-authorship network which shows research collaborations, are an important class of social networks. Research collaborations often yield good results but organizing a research group is a tedious task. Every researcher is concerned to collaborate with the best expertise complimenting him. Although there was abundant...
Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration...
As a promising direction of future Internet, Content-Centric Network (CCN) has attracted world-wide attention. In-network caching is an important feature of CCN network, which has significant impacts on the performance of content transmission. Existing researches on in-network caching either have no regard for the collaboration or need giant additional overhead for global optimization. This paper...
Recommendation systems have become extremely common in recent years due to the ubiquity of information across various applications. Online entertainment (e.g., Netflix), E-commerce (e.g., Amazon, Ebay) and publishing services such as Google News are all examples of services which use recommender systems. Recommendation systems are rapidly evolving in these years, but these methods have fallen short...
Collaborative filtering (CF) over large datasets requires significant computing power. Due to this data owning organizations often outsource the computation of CF (including some abstraction of the data itself) to a public cloud infrastructure. However, this leads to the question of how to verify the integrity of the outsourced computation. In this paper, we develop verification mechanisms for two...
In this research, we develop a hybrid recommendation system recommendation system for healthy living programs to patients with chronic diseases. Our experiments indicate that our model compared favorably against other real-world recommendation applications in terms of accuracy. We also demonstrated that the proposed hybrid algorithm performed better than traditional CF in terms of error rate, precision...
Lack of a comprehensive model for physical collaboration tasks motivated many researchers to focus on a particular aspect of the collaboration. In this work, we introduce a metric that quantifies the cooperative quality of a collaborative task. Based on the proposed model of cooperation, we present a set of five metrics that capture different properties of such tasks. The results of an empirical study...
Similarity measurement is a crucial process in collaborative filtering. User similarity is computed solely based on the numerical ratings of users. In this paper, we argue that the social information of users should be also taken into consideration to improve the performance of traditional similarity measurements. To achieve this, we propose a clustering-based similarity measurement approach incorporating...
Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users. However, such social network data often suffers from severe data sparsity and aren't able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Nowadays, trust prediction is...
Spectrum sensing data falsification (SSDF) attack are serious threats to collaborative spectrum sensing (CSS) of cognitive radio networks (CRNs). In this paper, inspired by EM (Expectation Maximization) method, we propose a scheme to estimate the presences of primary user (PU) and the SUs' operating point parameters (false alarm and detection probabilities) iteratively. The key features of the proposed...
Hybrid recommender systems combine different approaches to provide better recommendations. The most common hybrid algorithms mix collaborative, content-based, demographic filtering among others. However, these hybrid approaches seldom consider the user-recommender interaction. In this paper, we propose a new hybrid recommender system through considering the user-recommender interaction. First, we...
We perform social network analysis on movie producing teams formed by directors, producers and writers, using data from IMDb. We assemble an evolving social network by linking agents that worked together throughout history. After, we proceed to calculate topological and non-topological metrics from this network and its teams through time. We present the evolution of topological and non-topological...
In this paper, we analyze the co authorship networks of Latin American Computer Science research groups from 35 academic institutions in Argentina, Brazil, Chile, Colombia, Cuba, Mexico, Peru, Uruguay and Venezuela. Our analysis is based on data over a period of 20 years collected from DBLP, and aims to know the topological structure of each of these networks and provide a view of how they have evolved...
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