220 research outputs found
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search
Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale
data processing and analytics, particularly for analyzing multimedia contents
which are often of high dimensionality. Instead of using exact NN search,
extensive research efforts have been focusing on approximate NN search
algorithms. In this work, we present "HDIdx", an efficient high-dimensional
indexing library for fast approximate NN search, which is open-source and
written in Python. It offers a family of state-of-the-art algorithms that
convert input high-dimensional vectors into compact binary codes, making them
very efficient and scalable for NN search with very low space complexity
Federated Learning Attacks and Defenses: A Survey
In terms of artificial intelligence, there are several security and privacy
deficiencies in the traditional centralized training methods of machine
learning models by a server. To address this limitation, federated learning
(FL) has been proposed and is known for breaking down ``data silos" and
protecting the privacy of users. However, FL has not yet gained popularity in
the industry, mainly due to its security, privacy, and high cost of
communication. For the purpose of advancing the research in this field,
building a robust FL system, and realizing the wide application of FL, this
paper sorts out the possible attacks and corresponding defenses of the current
FL system systematically. Firstly, this paper briefly introduces the basic
workflow of FL and related knowledge of attacks and defenses. It reviews a
great deal of research about privacy theft and malicious attacks that have been
studied in recent years. Most importantly, in view of the current three
classification criteria, namely the three stages of machine learning, the three
different roles in federated learning, and the CIA (Confidentiality, Integrity,
and Availability) guidelines on privacy protection, we divide attack approaches
into two categories according to the training stage and the prediction stage in
machine learning. Furthermore, we also identify the CIA property violated for
each attack method and potential attack role. Various defense mechanisms are
then analyzed separately from the level of privacy and security. Finally, we
summarize the possible challenges in the application of FL from the aspect of
attacks and defenses and discuss the future development direction of FL
systems. In this way, the designed FL system has the ability to resist
different attacks and is more secure and stable.Comment: IEEE BigData. 10 pages, 2 figures, 2 table
A Lightweight Buyer-Seller Watermarking Protocol
The buyer-seller watermarking protocol enables a seller to successfully identify a traitor from a pirated copy, while preventing the seller from framing an innocent buyer. Based on finite field theory and the homomorphic property of public key cryptosystems such as RSA, several buyer-seller watermarking protocols (N. Memon and P. W. Wong (2001) and C.-L. Lei et al. (2004)) have been proposed previously. However, those protocols require not only large computational power but also substantial network bandwidth. In this paper, we introduce a new buyer-seller protocol that overcomes those weaknesses by managing the watermarks. Compared with the earlier protocols, ours is n times faster in terms of computation, where n is the number of watermark elements, while incurring only O(1/lN) times communication overhead given the finite field parameter lN. In addition, the quality of the watermarked image generated with our method is better, using the same watermark strength
- …