This paper presents our work to enhance the background music (BGM) in
DareFightingICE by adding adaptive features. The adaptive BGM consists of three
different categories of instruments playing the BGM of the winner sound design
from the 2022 DareFightingICE Competition. The BGM adapts by changing the
volume of each category of instruments. Each category is connected to a
different element of the game. We then run experiments to evaluate the adaptive
BGM by using a deep reinforcement learning AI agent that only uses audio as
input (Blind DL AI). The results show that the performance of the Blind DL AI
improves while playing with the adaptive BGM as compared to playing without the
adaptive BGM.

本文介绍了通过添加自适应特性来增强 DareFightingICE 背景音乐（BGM）的工作。自适应 BGM 由三个不同类别的乐器组成，播放来自 2022 DareFightingICE 比赛胜者声音设计的 BGM。通过改变每个类别乐器的音量来使 BGM 适应性变化。我们使用深度强化学习 AI 代理（盲 DL AI）进行实验评估自适应 BGM，而该代理仅使用声音作为输入。结果表明，与没有自适应 BGM 时相比，盲 DL AI 在播放自适应 BGM 时的性能有所改善。

提高游戏体验的格斗游戏自适应背景音乐

Fighting Game Adaptive Background Music for Improved Gameplay

Because most of the scientific literature data is unmarked, it makes semantic
representation learning based on unsupervised graph become crucial. At the same
time, in order to enrich the features of scientific literature, a learning
method of semantic representation of scientific literature based on adaptive
features and graph neural network is proposed. By introducing the adaptive
feature method, the features of scientific literature are considered globally
and locally. The graph attention mechanism is used to sum the features of
scientific literature with citation relationship, and give each scientific
literature different feature weights, so as to better express the correlation
between the features of different scientific literature. In addition, an
unsupervised graph neural network semantic representation learning method is
proposed. By comparing the mutual information between the positive and negative
local semantic representation of scientific literature and the global graph
semantic representation in the potential space, the graph neural network can
capture the local and global information, thus improving the learning ability
of the semantic representation of scientific literature. The experimental
results show that the proposed learning method of semantic representation of
scientific literature based on adaptive feature and graph neural network is
competitive on the basis of scientific literature classification, and has
achieved good results.

提出了一种基于自适应特征和图神经网络的科学文献语义表示学习方法，通过引入自适应特征方法，综合考虑了科学文献的全局和局部特征，借助图注意机制对具有引文关系的科学文献的特征进行加权求和，进一步表达不同科学文献特征之间的相关性，同时通过无监督图神经网络学习方法，提高了科学文献的语义表示学习能力，实验结果表明该方法在科学文献分类方面具有竞争力并取得了良好结果。

基于自适应特征和图神经网络的科学文献语义表示学习

Semantic Representation Learning of Scientific Literature based on  Adaptive Feature and Graph Neural Network

In this work, we introduce kernels with random Fourier features in the
meta-learning framework to leverage their strong few-shot learning ability. We
propose meta variational random features (MetaVRF) to learn adaptive kernels
for the base-learner, which is developed in a latent variable model by treating
the random feature basis as the latent variable. We formulate the optimization
of MetaVRF as a variational inference problem by deriving an evidence lower
bound under the meta-learning framework. To incorporate shared knowledge from
related tasks, we propose a context inference of the posterior, which is
established by an LSTM architecture. The LSTM-based inference network can
effectively integrate the context information of previous tasks with
task-specific information, generating informative and adaptive features. The
learned MetaVRF can produce kernels of high representational power with a
relatively low spectral sampling rate and also enables fast adaptation to new
tasks. Experimental results on a variety of few-shot regression and
classification tasks demonstrate that MetaVRF delivers much better, or at least
competitive, performance compared to existing meta-learning alternatives.

本文介绍了 MetaVRF，在元学习框架中引入随机傅里叶特征内核，利用它们强大的少样本学习能力。通过将随机特征基作为潜变量，我们提出了 MetaVRF 来学习自适应基学习器的内核。我们将 MetaVRF 的优化规定为一种变分推断问题，通过在元学习框架下推导证据下限。通过一个 LSTM 结构建立后验的上下文推断，以整合先前任务的上下文信息和任务特定信息，生成信息丰富且自适应的特征。实验结果表明，MetaVRF 在各种少样本回归和分类任务中的表现要好得多，或者至少与现有的元学习方法相媲美。