In this paper of a research based project, using Bidirectional Long
Short-Term Memory (BiLSTM) networks, we provide a novel Fight Scene Detection
(FSD) model which can be used for Movie Highlight Generation Systems (MHGS)
based on deep learning and Neural Networks . Movies usually have Fight Scenes
to keep the audience amazed. For trailer generation, or any other application
of Highlight generation, it is very tidious to first identify all such scenes
manually and then compile them to generate a highlight serving the purpose. Our
proposed FSD system utilises temporal characteristics of the movie scenes and
thus is capable to automatically identify fight scenes. Thereby helping in the
effective production of captivating movie highlights. We observe that the
proposed solution features 93.5% accuracy and is higher than 2D CNN with Hough
Forests which being 92% accurate and is significantly higher than 3D CNN which
features an accuracy of 65%.

基于双向长短期记忆网络 (BiLSTM) 的研究项目研究了战斗场景检测 (FSD) 模型，可用于基于深度学习和神经网络的电影精彩片段生成系统 (MHGS)。该系统利用电影场景的时间特征能够自动识别战斗场景，提高了引人入胜的电影精彩片段的有效制作。观察发现，该解决方案的准确率为 93.5%，高于 92% 准确率的二维 CNN 与 Hough 森林以及 65% 准确率的三维 CNN。

电影精彩片段生成系统中的打斗场景检测

Fight Scene Detection for Movie Highlight Generation System

Combinatorial optimization finds an optimal solution within a discrete set of
variables and constraints. The field has seen tremendous progress both in
research and industry. With the success of deep learning in the past decade, a
recent trend in combinatorial optimization has been to improve state-of-the-art
combinatorial optimization solvers by replacing key heuristic components with
machine learning (ML) models. In this paper, we investigate two essential
aspects of machine learning algorithms for combinatorial optimization: temporal
characteristics and attention. We argue that for the task of variable selection
in the branch-and-bound (B&B) algorithm, incorporating the temporal information
as well as the bipartite graph attention improves the solver's performance. We
support our claims with intuitions and numerical results over several standard
datasets used in the literature and competitions. Code is available at:
this https URL

通过替换启发式成分，结合机器学习算法的时间特征与注意力机制，优化了组合优化求解器在变量选择方面的性能。

带时序注意力图神经网络的精确组合优化

Exact Combinatorial Optimization with Temporo-Attentional Graph Neural  Networks

[Purpose] To better understand the online reviews and help potential
consumers, businessmen, and product manufacturers effectively obtain users'
evaluation on product aspects, this paper explores the distribution
regularities of user attention and sentiment toward product aspects from the
temporal perspective of online reviews. [Design/methodology/approach] Temporal
characteristics of online reviews (purchase time, review time, and time
intervals between purchase time and review time), similar attributes
clustering, and attribute-level sentiment computing technologies are employed
based on more than 340k smartphone reviews of three products from this http URL (a
famous online shopping platform in China) to explore the distribution
regularities of user attention and sentiment toward product aspects in this
article. [Findings] The empirical results show that a power-law distribution
can fit user attention to product aspects, and the reviews posted in short time
intervals contain more product aspects. Besides, the results show that the
values of user sentiment of product aspects are significantly higher/lower in
short time intervals which contribute to judging the advantages and weaknesses
of a product. [Research limitations] The paper can't acquire online reviews for
more products with temporal characteristics to verify the findings because of
the restriction on reviews crawling by the shopping platforms.
[Originality/value] This work reveals the distribution regularities of user
attention and sentiment toward product aspects, which is of great significance
in assisting decision-making, optimizing review presentation, and improving the
shopping experience.

通过对超过 340,000 篇关于智能手机的在线评论的分析，本文从时间角度出发，探索了用户对产品方面的关注度和情感的分布规律，并发现评论中短时间间隔内的产品方面往往有更高的情感值，这一研究成果对于优化评论展示和改善购物体验具有重要意义。