552 research outputs found
Knowledge sharing in virtual communities: a social exchange theory perspective
Purpose: The author tried to identify the knowledge sharing behaviors on the internet, using
structural equation modeling methods, proposing a model based on social exchange theory in
which share willingness, trust, reciprocity, altruism tended to have impact on people’s
knowledge sharing behaviors in virtual communities.
Design/methodology/approach: We presented an empirical research which integrated social
exchange theory and structural equation modeling methods to analyze several important factors
influencing members’ knowledge sharing behaviors in virtual communities.
Findings: We analyzed the knowledge sharing behaviors in virtual communities. We found that
members’ altruism can not predict knowledge sharing behaviors. We also found that members’
sharing willingness is the most important factor on virtual community knowledge sharing
behaviors compared with trust, reciprocity and altruism.
Originality/value: From the perspective of social exchange theory, we did empirical test and
verified the proposed research model by using structural equation modeling methods. Our
finding can help recognize people’s incentive about knowledge sharing.Peer Reviewe
Knowledge sharing in virtual communities: a social exchange theory perspective
Purpose: The author tried to identify the knowledge sharing behaviors on the internet, using
structural equation modeling methods, proposing a model based on social exchange theory in
which share willingness, trust, reciprocity, altruism tended to have impact on people’s
knowledge sharing behaviors in virtual communities.
Design/methodology/approach: We presented an empirical research which integrated social
exchange theory and structural equation modeling methods to analyze several important factors
influencing members’ knowledge sharing behaviors in virtual communities.
Findings: We analyzed the knowledge sharing behaviors in virtual communities. We found that
members’ altruism can not predict knowledge sharing behaviors. We also found that members’
sharing willingness is the most important factor on virtual community knowledge sharing
behaviors compared with trust, reciprocity and altruism.
Originality/value: From the perspective of social exchange theory, we did empirical test and
verified the proposed research model by using structural equation modeling methods. Our
finding can help recognize people’s incentive about knowledge sharing.Peer Reviewe
Knowledge sharing in virtual communities: a social exchange theory perspective
Purpose: The author tried to identify the knowledge sharing behaviors on the internet, using
structural equation modeling methods, proposing a model based on social exchange theory in
which share willingness, trust, reciprocity, altruism tended to have impact on people’s
knowledge sharing behaviors in virtual communities.
Design/methodology/approach: We presented an empirical research which integrated social
exchange theory and structural equation modeling methods to analyze several important factors
influencing members’ knowledge sharing behaviors in virtual communities.
Findings: We analyzed the knowledge sharing behaviors in virtual communities. We found that
members’ altruism can not predict knowledge sharing behaviors. We also found that members’
sharing willingness is the most important factor on virtual community knowledge sharing
behaviors compared with trust, reciprocity and altruism.
Originality/value: From the perspective of social exchange theory, we did empirical test and
verified the proposed research model by using structural equation modeling methods. Our
finding can help recognize people’s incentive about knowledge sharing.Peer Reviewe
A scalable location service for geographic ad hoc routing
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2001."January 2001."Includes bibliographical references (p. 55-57).GLS is a new distributed location service which tracks mobile node locations. GLS combined with geographic forwarding allows the construction of ad hoc mobile networks that scale to a larger number of nodes than possible with previous work. GLS is decentralized and runs on the mobile nodes themselves, requiring no fixed infrastructure. Each mobile node periodically updates a small set of other nodes (its location servers) with its current location. A node sends its position updates to its location servers without knowing their actual identities, assisted by a predefined ordering of node identifiers and a predefined geographic hierarchy. Queries for a mobile node's location also use the predefined identifier ordering and spatial hierarchy to find a location server for that node. Experiments using the ns simulator for up to 600 mobile nodes show that the storage and bandwidth requirements of GLS grow slowly with the size of the network. Furthermore, GLS tolerates node failures well: each failure has only a limited effect and query performance degrades gracefully as nodes fail and restart. The query performance of GLS is also relatively insensitive to node speeds. Simple geographic forwarding combined with GLS compares favorably with Dynamic Source Routing (DSR): in larger networks (over 200 nodes) our approach delivers more packets, but consumes fewer network resources.by Jinyang Li.S.M
SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection
Existing multi-focus image fusion (MFIF) methods often fail to preserve the
uncertain transition region and detect small focus areas within large defocused
regions accurately. To address this issue, this study proposes a new
small-area-aware MFIF algorithm for enhancing object detection capability.
First, we enhance the pixel attributes within the small focus and boundary
regions, which are subsequently combined with visual saliency detection to
obtain the pre-fusion results used to discriminate the distribution of focused
pixels. To accurately ensure pixel focus, we consider the source image as a
combination of focused, defocused, and uncertain regions and propose a
three-region segmentation strategy. Finally, we design an effective pixel
selection rule to generate segmentation decision maps and obtain the final
fusion results. Experiments demonstrated that the proposed method can
accurately detect small and smooth focus areas while improving object detection
performance, outperforming existing methods in both subjective and objective
evaluations. The source code is available at https://github.com/ixilai/SAMF.Comment: Accepted to International Conference on Acoustics, Speech and Signal
Processing (ICASSP) 202
Detection of Groups with Biased Representation in Ranking
Real-life tools for decision-making in many critical domains are based on
ranking results. With the increasing awareness of algorithmic fairness, recent
works have presented measures for fairness in ranking. Many of those
definitions consider the representation of different ``protected groups'', in
the top- ranked items, for any reasonable . Given the protected groups,
confirming algorithmic fairness is a simple task. However, the groups'
definitions may be unknown in advance. In this paper, we study the problem of
detecting groups with biased representation in the top- ranked items,
eliminating the need to pre-define protected groups. The number of such groups
possible can be exponential, making the problem hard. We propose efficient
search algorithms for two different fairness measures: global representation
bounds, and proportional representation. Then we propose a method to explain
the bias in the representations of groups utilizing the notion of Shapley
values. We conclude with an experimental study, showing the scalability of our
approach and demonstrating the usefulness of the proposed algorithms
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