23,985 research outputs found
An optimal feedback model to prevent manipulation behaviours in consensus under social network group decision making
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A novel framework to prevent manipulation behaviour
in consensus reaching process under social network
group decision making is proposed, which is based on a theoretically
sound optimal feedback model. The manipulation
behaviour classification is twofold: (1) ‘individual manipulation’
where each expert manipulates his/her own behaviour to achieve
higher importance degree (weight); and (2) ‘group manipulation’
where a group of experts force inconsistent experts to adopt
specific recommendation advices obtained via the use of fixed
feedback parameter. To counteract ‘individual manipulation’, a
behavioural weights assignment method modelling sequential
attitude ranging from ‘dictatorship’ to ‘democracy’ is developed,
and then a reasonable policy for group minimum adjustment cost
is established to assign appropriate weights to experts. To prevent
‘group manipulation’, an optimal feedback model with objective
function the individual adjustments cost and constraints related
to the threshold of group consensus is investigated. This approach
allows the inconsistent experts to balance group consensus and
adjustment cost, which enhances their willingness to adopt the
recommendation advices and consequently the group reaching
consensus on the decision making problem at hand. A numerical
example is presented to illustrate and verify the proposed optimal
feedback model
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Trust Management Model for Cloud Computing Environment
Software as a service or (SaaS) is a new software development and deployment
paradigm over the cloud and offers Information Technology services dynamically
as "on-demand" basis over the internet. Trust is one of the fundamental
security concepts on storing and delivering such services. In general, trust
factors are integrated into such existent security frameworks in order to add a
security level to entities collaborations through the trust relationship.
However, deploying trust factor in the secured cloud environment are more
complex engineering task due to the existence of heterogeneous types of service
providers and consumers. In this paper, a formal trust management model has
been introduced to manage the trust and its properties for SaaS in cloud
computing environment. The model is capable to represent the direct trust,
recommended trust, reputation etc. formally. For the analysis of the trust
properties in the cloud environment, the proposed approach estimates the trust
value and uncertainty of each peer by computing decay function, number of
positive interactions, reputation factor and satisfaction level for the
collected information.Comment: 5 Pages, 2 Figures, Conferenc
Quantify resilience enhancement of UTS through exploiting connect community and internet of everything emerging technologies
This work aims at investigating and quantifying the Urban Transport System
(UTS) resilience enhancement enabled by the adoption of emerging technology
such as Internet of Everything (IoE) and the new trend of the Connected
Community (CC). A conceptual extension of Functional Resonance Analysis Method
(FRAM) and its formalization have been proposed and used to model UTS
complexity. The scope is to identify the system functions and their
interdependencies with a particular focus on those that have a relation and
impact on people and communities. Network analysis techniques have been applied
to the FRAM model to identify and estimate the most critical community-related
functions. The notion of Variability Rate (VR) has been defined as the amount
of output variability generated by an upstream function that can be
tolerated/absorbed by a downstream function, without significantly increasing
of its subsequent output variability. A fuzzy based quantification of the VR on
expert judgment has been developed when quantitative data are not available.
Our approach has been applied to a critical scenario (water bomb/flash
flooding) considering two cases: when UTS has CC and IoE implemented or not.
The results show a remarkable VR enhancement if CC and IoE are deploye
Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach
Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy
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