292 research outputs found
Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?
Most people consider their friends to be more positive than themselves,
exhibiting a Sentiment Paradox. Psychology research attributes this paradox to
human cognition bias. With the goal to understand this phenomenon, we study
sentiment paradoxes in social networks. Our work shows that social connections
(friends, followees, or followers) of users are indeed (not just illusively)
more positive than the users themselves. This is mostly due to positive users
having more friends. We identify five sentiment paradoxes at different network
levels ranging from triads to large-scale communities. Empirical and
theoretical evidence are provided to validate the existence of such sentiment
paradoxes. By investigating the relationships between the sentiment paradox and
other well-developed network paradoxes, i.e., friendship paradox and activity
paradox, we find that user sentiments are positively correlated to their number
of friends but rarely to their social activity. Finally, we demonstrate how
sentiment paradoxes can be used to predict user sentiments.Comment: The 14th International AAAI Conference on Web and Social Media (ICWSM
2020
Performance Evaluation for Subarray-based Reconfigurable Intelligent Surface-Aided Wireless Communication Systems
Reconfigurable intelligent surfaces (RISs) have received extensive concern to
improve the performance of wireless communication systems. In this paper, a
subarray-based scheme is investigated in terms of its effects on ergodic
spectral efficiency (SE) and energy efficiency (EE) in RIS-assisted systems. In
this scheme, the adjacent elements divided into a subarray are controlled by
one signal and share the same reflection coefficient. An upper bound of ergodic
SE is derived and an optimal phase shift design is proposed for the
subarray-based RIS. Based on the upper bound and optimal design, we obtain the
maximum of the upper bound. In particular, we analytically evaluate the effect
of the subarray-based RIS on EE since it reduces SE and power consumption
simultaneously. Numerical results verify the tightness of the upper bound,
demonstrate the effectiveness of the optimal phase shift design for the
subarray-based RIS, and reveal the effects of the subarray-based scheme on SE
and EE.Comment: 6 pages, 4 figures, accepted by IEEE GLOBECOM 202
DANAA: Towards transferable attacks with double adversarial neuron attribution
While deep neural networks have excellent results in many fields, they are
susceptible to interference from attacking samples resulting in erroneous
judgments. Feature-level attacks are one of the effective attack types, which
targets the learnt features in the hidden layers to improve its transferability
across different models. Yet it is observed that the transferability has been
largely impacted by the neuron importance estimation results. In this paper, a
double adversarial neuron attribution attack method, termed `DANAA', is
proposed to obtain more accurate feature importance estimation. In our method,
the model outputs are attributed to the middle layer based on an adversarial
non-linear path. The goal is to measure the weight of individual neurons and
retain the features that are more important towards transferability. We have
conducted extensive experiments on the benchmark datasets to demonstrate the
state-of-the-art performance of our method. Our code is available at:
https://github.com/Davidjinzb/DANAAComment: Accepted by 19th International Conference on Advanced Data Mining and
Applications. (ADMA 2023
Piecewise linear regression-based single image super-resolution via Hadamard transform
Image super-resolution (SR) has extensive applications in surveillance systems, satellite imaging, medical imaging, and ultra-high definition display devices. The state-ofthe-art methods for SR still incur considerable running time. In this paper, we propose a novel approach based on Hadamard pattern and tree search structure in order to reduce the running time significantly. In this approach, LR (low-resolution)-HR (high-resolution) training patch pairs are classified into different classes based on the Hadamard patterns generated from the LR training patches. The mapping relationship between the LR space and the HR space for each class is then learned and used for SR. Experimental results show that the proposed method can achieve comparable accuracy as state-of-the-art methods with much faster running speed
Panoptic segmentation-based attention for image captioning
Image captioning is the task of generating textual descriptions of images. In order to obtain a better image representation, attention mechanisms have been widely adopted in image captioning. However, in existing models with detection-based attention, the rectangular attention regions are not fine-grained, as they contain irrelevant regions (e.g., background or overlapped regions) around the object, making the model generate inaccurate captions. To address this issue, we propose panoptic segmentation-based attention that performs attention at a mask-level (i.e., the shape of the main part of an instance). Our approach extracts feature vectors from the corresponding segmentation regions, which is more fine-grained than current attention mechanisms. Moreover, in order to process features of different classes independently, we propose a dual-attention module which is generic and can be applied to other frameworks. Experimental results showed that our model could recognize the overlapped objects and understand the scene better. Our approach achieved competitive performance against state-of-the-art methods. We made our code available
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