We proposed an Attentive Recurrent Neural Network (ARNN), which recurrently
applies attention layers along a sequence and has linear complexity with
respect to the sequence length. The proposed model operates on multi-channel
EEG signals rather than single channel signals and leverages parallel
computation. In this cell, the attention layer is a computational unit that
efficiently applies self-attention and cross-attention mechanisms to compute a
recurrent function over a wide number of state vectors and input signals. Our
architecture is inspired in part by the attention layer and long short-term
memory (LSTM) cells, and it uses long-short style gates, but it scales this
typical cell up by several orders to parallelize for multi-channel EEG signals.
It inherits the advantages of attention layers and LSTM gate while avoiding
their respective drawbacks. We evaluated the model effectiveness through
extensive experiments with heterogeneous datasets, including the CHB-MIT and
UPenn and Mayos Clinic, CHB-MIT datasets. The empirical findings suggest that
the ARNN model outperforms baseline methods such as LSTM, Vision Transformer
(ViT), Compact Convolution Transformer (CCT), and R-Transformer (RT),
showcasing superior performance and faster processing capabilities across a
wide range of tasks. The code has been made publicly accessible at
https://github.com/Salim-Lysiun/ARNN.

我们提出了一种注意力循环神经网络 (ARNN)，它在序列上循环应用注意力层，并具有与序列长度线性复杂度的特点。该模型在多通道脑电图信号上操作，利用并行计算。我们的架构受注意力层和长短期记忆 (LSTM) 单元的启发，它使用长短样式门进行扩展，并通过数倍的扩展来并行化处理多通道脑电图信号。经过广泛的实验评估，包括 CHB-MIT、UPenn 和 Mayos Clinic CHB-MIT 数据集，实证结果表明 ARNN 模型在各种任务中表现优于 LSTM、Vision Transformer (ViT)、Compact Convolution Transformer (CCT) 和 R-Transformer (RT) 等基准方法，展示出卓越的性能和更快的处理能力。代码已在 https://github.com/Salim-Lysiun/ARNN 上公开获取。

ARNN：用于识别癫痫发作的多通道脑电信号的注意力循环神经网络

ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals  to Identify Epileptic Seizures

As influencers play considerable roles in social media marketing, companies
increase the budget for influencer marketing. Hiring effective influencers is
crucial in social influencer marketing, but it is challenging to find the right
influencers among hundreds of millions of social media users. In this paper, we
propose InfluencerRank that ranks influencers by their effectiveness based on
their posting behaviors and social relations over time. To represent the
posting behaviors and social relations, the graph convolutional neural networks
are applied to model influencers with heterogeneous networks during different
historical periods. By learning the network structure with the embedded node
features, InfluencerRank can derive informative representations for influencers
at each period. An attentive recurrent neural network finally distinguishes
highly effective influencers from other influencers by capturing the knowledge
of the dynamics of influencer representations over time. Extensive experiments
have been conducted on an Instagram dataset that consists of 18,397 influencers
with their 2,952,075 posts published within 12 months. The experimental results
demonstrate that InfluencerRank outperforms existing baseline methods. An
in-depth analysis further reveals that all of our proposed features and model
components are beneficial to discover effective influencers.

本文提出 InfluencerRank，使用图卷积神经网络和注意力机制的循环神经网络来排名社交媒体上的影响者，实验结果显示其优于现有的基准方法，可以作为发现高效影响者的有效工具。