217 research outputs found
Towards Profit Maximization for Online Social Network Providers
Online Social Networks (OSNs) attract billions of users to share information
and communicate where viral marketing has emerged as a new way to promote the
sales of products. An OSN provider is often hired by an advertiser to conduct
viral marketing campaigns. The OSN provider generates revenue from the
commission paid by the advertiser which is determined by the spread of its
product information. Meanwhile, to propagate influence, the activities
performed by users such as viewing video ads normally induce diffusion cost to
the OSN provider. In this paper, we aim to find a seed set to optimize a new
profit metric that combines the benefit of influence spread with the cost of
influence propagation for the OSN provider. Under many diffusion models, our
profit metric is the difference between two submodular functions which is
challenging to optimize as it is neither submodular nor monotone. We design a
general two-phase framework to select seeds for profit maximization and develop
several bounds to measure the quality of the seed set constructed. Experimental
results with real OSN datasets show that our approach can achieve high
approximation guarantees and significantly outperform the baseline algorithms,
including state-of-the-art influence maximization algorithms.Comment: INFOCOM 2018 (Full version), 12 page
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Influence Maximization is an extensively-studied problem that targets at
selecting a set of initial seed nodes in the Online Social Networks (OSNs) to
spread the influence as widely as possible. However, it remains an open
challenge to design fast and accurate algorithms to find solutions in
large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not
scalable, while other heuristic algorithms do not have any theoretical
guarantee and they have been shown to produce poor solutions for quite some
cases. In this paper, we propose hop-based algorithms that can easily scale to
millions of nodes and billions of edges. Unlike previous heuristics, our
proposed hop-based approaches can provide certain theoretical guarantees.
Experimental evaluations with real OSN datasets demonstrate the efficiency and
effectiveness of our algorithms.Comment: Extended version of the conference paper at ASONAM 2017, 11 page
Kervolutional Neural Networks
Convolutional neural networks (CNNs) have enabled the state-of-the-art
performance in many computer vision tasks. However, little effort has been
devoted to establishing convolution in non-linear space. Existing works mainly
leverage on the activation layers, which can only provide point-wise
non-linearity. To solve this problem, a new operation, kervolution (kernel
convolution), is introduced to approximate complex behaviors of human
perception systems leveraging on the kernel trick. It generalizes convolution,
enhances the model capacity, and captures higher order interactions of
features, via patch-wise kernel functions, but without introducing additional
parameters. Extensive experiments show that kervolutional neural networks (KNN)
achieve higher accuracy and faster convergence than baseline CNN.Comment: oral paper in CVPR 201
Non-iterative RGB-D-inertial Odometry
This paper presents a non-iterative solution to RGB-D-inertial odometry
system. Traditional odometry methods resort to iterative algorithms which are
usually computationally expensive or require well-designed initialization. To
overcome this problem, this paper proposes to combine a non-iterative front-end
(odometry) with an iterative back-end (loop closure) for the RGB-D-inertial
SLAM system. The main contribution lies in the novel non-iterative front-end,
which leverages on inertial fusion and kernel cross-correlators (KCC) to match
point clouds in frequency domain. Dominated by the fast Fourier transform
(FFT), our method is only of complexity , where is
the number of points. Map fusion is conducted by element-wise operations, so
that both time and space complexity are further reduced. Extensive experiments
show that, due to the lightweight of the proposed front-end, the framework is
able to run at a much faster speed yet still with comparable accuracy with the
state-of-the-arts
Robust short clip representation and fast search through large video collections
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