560,935 research outputs found
On learning optimized reaction diffusion processes for effective image restoration
For several decades, image restoration remains an active research topic in
low-level computer vision and hence new approaches are constantly emerging.
However, many recently proposed algorithms achieve state-of-the-art performance
only at the expense of very high computation time, which clearly limits their
practical relevance. In this work, we propose a simple but effective approach
with both high computational efficiency and high restoration quality. We extend
conventional nonlinear reaction diffusion models by several parametrized linear
filters as well as several parametrized influence functions. We propose to
train the parameters of the filters and the influence functions through a loss
based approach. Experiments show that our trained nonlinear reaction diffusion
models largely benefit from the training of the parameters and finally lead to
the best reported performance on common test datasets for image restoration.
Due to their structural simplicity, our trained models are highly efficient and
are also well-suited for parallel computation on GPUs.Comment: 9 pages, 3 figures, 3 tables. CVPR2015 oral presentation together
with the supplemental material of 13 pages, 8 pages (Notes on diffusion
networks
Topic-Based Influence Computation in Social Networks under Resource Constraints
As social networks are constantly changing and evolving, methods to analyze
dynamic social networks are becoming more important in understanding social
trends. However, due to the restrictions imposed by the social network service
providers, the resources available to fetch the entire contents of a social
network are typically very limited. As a result, analysis of dynamic social
network data requires maintaining an approximate copy of the social network for
each time period, locally. In this paper, we study the problem of dynamic
network and text fetching with limited probing capacities, for identifying and
maintaining influential users as the social network evolves. We propose an
algorithm to probe the relationships (required for global influence
computation) as well as posts (required for topic-based influence computation)
of a limited number of users during each probing period, based on the influence
trends and activities of the users. We infer the current network based on the
newly probed user data and the last known version of the network maintained
locally. Additionally, we propose to use link prediction methods to further
increase the accuracy of our network inference. We employ PageRank as the
metric for influence computation. We illustrate how the proposed solution
maintains accurate PageRank scores for computing global influence, and
topic-sensitive weighted PageRank scores for topic-based influence. The latter
relies on a topic-based network constructed via weights determined by semantic
analysis of posts and their sharing statistics. We evaluate the effectiveness
of our algorithms by comparing them with the true influence scores of the full
and up-to-date version of the network, using data from the micro-blogging
service Twitter. Results show that our techniques significantly outperform
baseline methods and are superior to state-of-the-art techniques from the
literature
Handling Mobility in Dense Networks
Network densification is one of key technologies in future networks to
significantly increase network capacity. The gain obtained by network
densification for fixed terminals have been studied and proved. However for
mobility users, there are a number of issues, such as more frequent handover,
packet loss due to high mobility, interference management and so on. The
conventional solutions are to handover high speed mobiles to macro base
stations or multicast traffic to multiple base stations. These solutions fail
to exploit the capacity of dense networks and overuse the backhaul capacity. In
this paper we propose a set of solutions to systematically solve the technical
challenges of mobile dense networks. We introduce network architecture together
with data transmission protocols to support mobile users. A software-defined
protocol (SDP) concept is presented so that combinations of transport protocols
and physical layer functions can be optimized and triggered on demand. Our
solutions can significantly boost performance of dense networks and simplify
the packet handling process. Importantly, the gain brought by network
densification to fixed users can also be achieved for mobile users
Improving Interpretability of Deep Neural Networks with Semantic Information
Interpretability of deep neural networks (DNNs) is essential since it enables
users to understand the overall strengths and weaknesses of the models, conveys
an understanding of how the models will behave in the future, and how to
diagnose and correct potential problems. However, it is challenging to reason
about what a DNN actually does due to its opaque or black-box nature. To
address this issue, we propose a novel technique to improve the
interpretability of DNNs by leveraging the rich semantic information embedded
in human descriptions. By concentrating on the video captioning task, we first
extract a set of semantically meaningful topics from the human descriptions
that cover a wide range of visual concepts, and integrate them into the model
with an interpretive loss. We then propose a prediction difference maximization
algorithm to interpret the learned features of each neuron. Experimental
results demonstrate its effectiveness in video captioning using the
interpretable features, which can also be transferred to video action
recognition. By clearly understanding the learned features, users can easily
revise false predictions via a human-in-the-loop procedure.Comment: To appear in CVPR 201
Contributive Social Capital Extraction From Different Types of Online Data Sources
It is a recurring problem of online communication that the properties of
unknown people are hard to assess. This may lead to various issues such as the
spread of `fake news' from untrustworthy sources. In sociology the sum of
(social) resources available to a person through their social network is often
described as social capital. In this article, we look at social capital from a
different angle. Instead of evaluating the advantage that people have because
of their membership in a certain group, we investigate various ways to infer
the social capital a person adds or may add to the network, their contributive
social capital (CSC). As there is no consensus in the literature on what the
social capital of a person exactly consists of, we look at various related
properties: expertise, reputation, trustworthiness, and influence. The analysis
of these features is investigated for five different sources of online data:
microblogging (e.g., Twitter), social networking platforms (e.g., Facebook),
direct communication (e.g., email), scientometrics, and threaded discussion
boards (e.g., Reddit). In each field we discuss recent publications and put a
focus on the data sources used, the algorithms implemented, and the performance
evaluation. The findings are compared and set in context to contributive social
capital extraction. The analysis algorithms are based on individual features
(e.g., followers on Twitter), ratios thereof, or a person's centrality measures
(e.g., PageRank). The machine learning approaches, such as straightforward
classifiers (e.g., support vector machines) use ground truths that are
connected to social capital. The discussion of these methods is intended to
facilitate research on the topic by identifying relevant data sources and the
best suited algorithms, and by providing tested methods for the evaluation of
findings.Comment: 44 page
Which is better? A Modularized Evaluation for Topic Popularity Prediction
Topic popularity prediction in social networks has drawn much attention
recently. Various elegant models have been proposed for this issue. However,
different datasets and evaluation metrics they use lead to low comparability.
So far there is no unified scheme to evaluate them, making it difficult to
select and compare models. We conduct a comprehensible survey, propose an
evaluation scheme and apply it to existing methods. Our scheme consists of four
modules: classification; qualitative evaluation on several metrics;
quantitative experiment on real world data; final ranking with risk matrix and
to reflect performances under different scenarios.
Furthermore, we analyze the efficiency and contribution of features used in
feature oriented methods. The results show that feature oriented methods are
more suitable for scenarios requiring high accuracy, while relation based
methods have better consistency. Our work helps researchers compare and choose
methods appropriately, and provides insights for further improvements
User-level Weibo Recommendation incorporating Social Influence based on Semi-Supervised Algorithm
Tencent Weibo, as one of the most popular micro-blogging services in China,
has attracted millions of users, producing 30-60 millions of weibo (similar as
tweet in Twitter) daily. With the overload problem of user generate content,
Tencent users find it is more and more hard to browse and find valuable
information at the first time. In this paper, we propose a Factor Graph based
weibo recommendation algorithm TSI-WR (Topic-Level Social Influence based Weibo
Recommendation), which could help Tencent users to find most suitable
information. The main innovation is that we consider both direct and indirect
social influence from topic level based on social balance theory. The main
advantages of adopting this strategy are that it could first build a more
accurate description of latent relationship between two users with weak
connections, which could help to solve the data sparsity problem; second
provide a more accurate recommendation for a certain user from a wider range.
Other meaningful contextual information is also combined into our model, which
include: Users profile, Users influence, Content of weibos, Topic information
of weibos and etc. We also design a semi-supervised algorithm to further reduce
the influence of data sparisty. The experiments show that all the selected
variables are important and the proposed model outperforms several baseline
methods.Comment: to be sumitted in JASIS
Self-Supervised Representation Learning on Document Images
This work analyses the impact of self-supervised pre-training on document
images in the context of document image classification. While previous
approaches explore the effect of self-supervision on natural images, we show
that patch-based pre-training performs poorly on document images because of
their different structural properties and poor intra-sample semantic
information. We propose two context-aware alternatives to improve performance
on the Tobacco-3482 image classification task. We also propose a novel method
for self-supervision, which makes use of the inherent multi-modality of
documents (image and text), which performs better than other popular
self-supervised methods, including supervised ImageNet pre-training, on
document image classification scenarios with a limited amount of data.Comment: 15 pages, 5 figures. Accepted at DAS 2020: IAPR International
Workshop on Document Analysis System
Deep Temporal Sigmoid Belief Networks for Sequence Modeling
Deep dynamic generative models are developed to learn sequential dependencies
in time-series data. The multi-layered model is designed by constructing a
hierarchy of temporal sigmoid belief networks (TSBNs), defined as a sequential
stack of sigmoid belief networks (SBNs). Each SBN has a contextual hidden
state, inherited from the previous SBNs in the sequence, and is used to
regulate its hidden bias. Scalable learning and inference algorithms are
derived by introducing a recognition model that yields fast sampling from the
variational posterior. This recognition model is trained jointly with the
generative model, by maximizing its variational lower bound on the
log-likelihood. Experimental results on bouncing balls, polyphonic music,
motion capture, and text streams show that the proposed approach achieves
state-of-the-art predictive performance, and has the capacity to synthesize
various sequences.Comment: to appear in NIPS 201
"Birds of a Feather": Does User Homophily Impact Information Diffusion in Social Media?
This article investigates the impact of user homophily on the social process
of information diffusion in online social media. Over several decades, social
scientists have been interested in the idea that similarity breeds connection:
precisely known as "homophily". Homophily has been extensively studied in the
social sciences and refers to the idea that users in a social system tend to
bond more with ones who are similar to them than to ones who are dissimilar.
The key observation is that homophily structures the ego-networks of
individuals and impacts their communication behavior. It is therefore likely to
effect the mechanisms in which information propagates among them. To this
effect, we investigate the interplay between homophily along diverse user
attributes and the information diffusion process on social media. In our
approach, we first extract diffusion characteristics---corresponding to the
baseline social graph as well as graphs filtered on different user attributes
(e.g. location, activity). Second, we propose a Dynamic Bayesian Network based
framework to predict diffusion characteristics at a future time. Third, the
impact of attribute homophily is quantified by the ability of the predicted
characteristics in explaining actual diffusion, and external variables,
including trends in search and news. Experimental results on a large Twitter
dataset demonstrate that choice of the homophilous attribute can impact the
prediction of information diffusion, given a specific metric and a topic. In
most cases, attribute homophily is able to explain the actual diffusion and
external trends by ~15-25% over cases when homophily is not considered.Comment: 31 pages, 10 figures, 3 table
- …