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Self-organizing peer-to-peer social networks
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin
NewsMe: A case study for adaptive news systems with open user model
Adaptive news systems have become important in recent years. A lot of work has been put into developing these adaptation processes. We describe here an adaptive news system application, which uses an open user model and allow users to manipulate their interest profiles. We also present a study of the system. Our results showed that user profile manipulation should be used with caution. © 2007 IEEE
Adaptive Representations for Tracking Breaking News on Twitter
Twitter is often the most up-to-date source for finding and tracking breaking
news stories. Therefore, there is considerable interest in developing filters
for tweet streams in order to track and summarize stories. This is a
non-trivial text analytics task as tweets are short, and standard retrieval
methods often fail as stories evolve over time. In this paper we examine the
effectiveness of adaptive mechanisms for tracking and summarizing breaking news
stories. We evaluate the effectiveness of these mechanisms on a number of
recent news events for which manually curated timelines are available.
Assessments based on ROUGE metrics indicate that an adaptive approaches are
best suited for tracking evolving stories on Twitter.Comment: 8 Pag
Online Product Quantization
Approximate nearest neighbor (ANN) search has achieved great success in many
tasks. However, existing popular methods for ANN search, such as hashing and
quantization methods, are designed for static databases only. They cannot
handle well the database with data distribution evolving dynamically, due to
the high computational effort for retraining the model based on the new
database. In this paper, we address the problem by developing an online product
quantization (online PQ) model and incrementally updating the quantization
codebook that accommodates to the incoming streaming data. Moreover, to further
alleviate the issue of large scale computation for the online PQ update, we
design two budget constraints for the model to update partial PQ codebook
instead of all. We derive a loss bound which guarantees the performance of our
online PQ model. Furthermore, we develop an online PQ model over a sliding
window with both data insertion and deletion supported, to reflect the
real-time behaviour of the data. The experiments demonstrate that our online PQ
model is both time-efficient and effective for ANN search in dynamic large
scale databases compared with baseline methods and the idea of partial PQ
codebook update further reduces the update cost.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering
(DOI: 10.1109/TKDE.2018.2817526
Using Markov Chains for link prediction in adaptive web sites
The large number of Web pages on many Web sites has raised
navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past
visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability
matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site
Bags of Affine Subspaces for Robust Object Tracking
We propose an adaptive tracking algorithm where the object is modelled as a
continuously updated bag of affine subspaces, with each subspace constructed
from the object's appearance over several consecutive frames. In contrast to
linear subspaces, affine subspaces explicitly model the origin of subspaces.
Furthermore, instead of using a brittle point-to-subspace distance during the
search for the object in a new frame, we propose to use a subspace-to-subspace
distance by representing candidate image areas also as affine subspaces.
Distances between subspaces are then obtained by exploiting the non-Euclidean
geometry of Grassmann manifolds. Experiments on challenging videos (containing
object occlusions, deformations, as well as variations in pose and
illumination) indicate that the proposed method achieves higher tracking
accuracy than several recent discriminative trackers.Comment: in International Conference on Digital Image Computing: Techniques
and Applications, 201
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