1,124 research outputs found
A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation
Trust-based celebrity user identification is the key to the industry\u27s reputation for electronic word of mouth. However, trust and mistrust are independent and coexistent concepts. In this context, we need to consider the existence of the two kinds of user relations brought about by the impact. This paper analyzes the characteristics of trust and distrust in social networks, and gives formal descriptions of trust networks, untrusted networks, and mixed trust networks. Based on the indicators such as degree distribution, correlation coefficient, and matching coefficient, the structural properties of mixed trust networks are studied. Based on the PageRank algorithm, the HTMM metrics affecting users under the mixed trust network environment are proposed. Finally, the validity of HTMM is verified through a real data set containing trust and distrust. Experimental results show that the results of HTMM\u27s celebrity user identification method still have a low level of trust
Equivariant Light Field Convolution and Transformer
3D reconstruction and novel view rendering can greatly benefit from geometric
priors when the input views are not sufficient in terms of coverage and
inter-view baselines. Deep learning of geometric priors from 2D images often
requires each image to be represented in a canonical frame and the prior
to be learned in a given or learned canonical frame. In this paper, given
only the relative poses of the cameras, we show how to learn priors from
multiple views equivariant to coordinate frame transformations by proposing an
-equivariant convolution and transformer in the space of rays in 3D.
This enables the creation of a light field that remains equivariant to the
choice of coordinate frame. The light field as defined in our work, refers both
to the radiance field and the feature field defined on the ray space. We model
the ray space, the domain of the light field, as a homogeneous space of
and introduce the -equivariant convolution in ray space. Depending on
the output domain of the convolution, we present convolution-based
-equivariant maps from ray space to ray space and to . Our
mathematical framework allows us to go beyond convolution to
-equivariant attention in the ray space. We demonstrate how to tailor
and adapt the equivariant convolution and transformer in the tasks of
equivariant neural rendering and reconstruction from multiple views. We
demonstrate -equivariance by obtaining robust results in roto-translated
datasets without performing transformation augmentation.Comment: 46 page
The Impact of Third-party Payments on Chinese Commercial Bank Profitability
In recent years, China's Internet finance has developed rapidly, especially the third-party payment, which has experienced explosive growth in transaction size. The emergence and development of third-party payment platforms not only prompted Chinese commercial banks to carry out financial innovation but also made commercial banks face the challenge of customer loss and deposit loss. At the same time, third-party payment companies use the network platform to provide professional financial services such as professional loans and wealth management to more and more financial consumers and may also affect the profits of commercial banks. At present, the research literature on the relationship between commercial banks and third-party payment is scarce, especially in empirical research. Therefore, this paper mainly discusses the direction and extent of influence of third-party payment on Chinese commercial banks. This paper examines the bank performance of 67 commercial banks in China from 2011 to 2016, using the dynamic panel data model to study the impact of third-party payments on the profits of Chinese state-owned commercial banks, Chinese joint-stock commercial banks and Chinese city commercial banks.The empirical results show that the impact of third-party mobile payment on China's city commercial banks and joint-stock commercial banks is positive, but not significant. However, third-party Internet payments have been found to have a significant negative impact on China's joint-stock commercial banks and city commercial banks. Because the number of Chinese state-owned commercial banks used in this paper is too small, resulting in insufficient empirical samples, most of China's state-owned commercial banks are supported by the Chinese government, and their status is difficult to shake, this paper does not summarize the impact of third-party payments on the profits of state-owned commercial banks
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Few-shot learning (FSL) aims to develop a learning model with the ability to
generalize to new classes using a few support samples. For transductive FSL
tasks, prototype learning and label propagation methods are commonly employed.
Prototype methods generally first learn the representative prototypes from the
support set and then determine the labels of queries based on the metric
between query samples and prototypes. Label propagation methods try to
propagate the labels of support samples on the constructed graph encoding the
relationships between both support and query samples. This paper aims to
integrate these two principles together and develop an efficient and robust
transductive FSL approach, termed Prototype-based Soft-label Propagation
(PSLP). Specifically, we first estimate the soft-label presentation for each
query sample by leveraging prototypes. Then, we conduct soft-label propagation
on our learned query-support graph. Both steps are conducted progressively to
boost their respective performance. Moreover, to learn effective prototypes for
soft-label estimation as well as the desirable query-support graph for
soft-label propagation, we design a new joint message passing scheme to learn
sample presentation and relational graph jointly. Our PSLP method is
parameter-free and can be implemented very efficiently. On four popular
datasets, our method achieves competitive results on both balanced and
imbalanced settings compared to the state-of-the-art methods. The code will be
released upon acceptance
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
Deep learning (DL) methods have been widely applied to anomaly-based network
intrusion detection system (NIDS) to detect malicious traffic. To expand the
usage scenarios of DL-based methods, the federated learning (FL) framework
allows multiple users to train a global model on the basis of respecting
individual data privacy. However, it has not yet been systematically evaluated
how robust FL-based NIDSs are against existing privacy attacks under existing
defenses. To address this issue, we propose two privacy evaluation metrics
designed for FL-based NIDSs, including (1) privacy score that evaluates the
similarity between the original and recovered traffic features using
reconstruction attacks, and (2) evasion rate against NIDSs using Generative
Adversarial Network-based adversarial attack with the reconstructed benign
traffic. We conduct experiments to show that existing defenses provide little
protection that the corresponding adversarial traffic can even evade the SOTA
NIDS Kitsune. To defend against such attacks and build a more robust FL-based
NIDS, we further propose FedDef, a novel optimization-based input perturbation
defense strategy with theoretical guarantee. It achieves both high utility by
minimizing the gradient distance and strong privacy protection by maximizing
the input distance. We experimentally evaluate four existing defenses on four
datasets and show that our defense outperforms all the baselines in terms of
privacy protection with up to 7 times higher privacy score, while maintaining
model accuracy loss within 3% under optimal parameter combination.Comment: 14 pages, 9 figures, submitted to TIF
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