Minimizing response times is crucial for emergency medical services to reduce
patients' waiting times and to increase their survival rates. Many models exist
to optimize operational tasks such as ambulance allocation and dispatching.
Including accurate demand forecasts in such models can improve operational
decision-making. Against this background, we present a novel convolutional
neural network (CNN) architecture that transforms time series data into
heatmaps to predict ambulance demand. Applying such predictions requires
incorporating external features that influence ambulance demands. We contribute
to the existing literature by providing a flexible, generic CNN architecture,
allowing for the inclusion of external features with varying dimensions.
Additionally, we provide a feature selection and hyperparameter optimization
framework utilizing Bayesian optimization. We integrate historical ambulance
demand and external information such as weather, events, holidays, and time. To
show the superiority of the developed CNN architecture over existing
approaches, we conduct a case study for Seattle's 911 call data and include
external information. We show that the developed CNN architecture outperforms
existing state-of-the-art methods and industry practice by more than 9%.

研究了应用卷积神经网络 (CNN) 把时间序列数据转换为热力图来预测救护车需求，结合历史救护车需求和外部信息如天气、事件、假日和时间等，提供了一个灵活的、通用的 CNN 架构，并使用贝叶斯优化框架进行特征选择和超参数优化。在西雅图的 911 呼叫数据上进行了实验，表明该 CNN 架构的性能比现有技术和行业实践提高了超过 9%。

卷积神经网络预测救护车需求

Ambulance Demand Prediction via Convolutional Neural Networks

Obtaining a well-trained model involves expensive data collection and
training procedures, therefore the model is a valuable intellectual property.
Recent studies revealed that adversaries can `steal' deployed models even when
they have no training samples and can not get access to the model parameters or
structures. Currently, there were some defense methods to alleviate this
threat, mostly by increasing the cost of model stealing. In this paper, we
explore the defense from another angle by verifying whether a suspicious model
contains the knowledge of defender-specified \emph{external features}.
Specifically, we embed the external features by tempering a few training
samples with style transfer. We then train a meta-classifier to determine
whether a model is stolen from the victim. This approach is inspired by the
understanding that the stolen models should contain the knowledge of features
learned by the victim model. We examine our method on both CIFAR-10 and
ImageNet datasets. Experimental results demonstrate that our method is
effective in detecting different types of model stealing simultaneously, even
if the stolen model is obtained via a multi-stage stealing process. The codes
for reproducing main results are available at Github
(this https URL).

本文从外部特征的角度出发，通过渗透几个经过风格转换的训练样本嵌入外部特征，训练元分类器验证一个可疑模型是否包含被攻击者指定的外部特征知识，实验结果表明我们的方法可以有效地同时检测不同类型的模型窃取，即使窃取的模型是通过多阶段窃取过程获得的。

通过验证嵌入的外部特征防御模型窃取

Defending against Model Stealing via Verifying Embedded External  Features

Identifying the veracity of a news article is an interesting problem while
automating this process can be a challenging task. Detection of a news article
as fake is still an open question as it is contingent on many factors which the
current state-of-the-art models fail to incorporate. In this paper, we explore
a subtask to fake news identification, and that is stance detection. Given a
news article, the task is to determine the relevance of the body and its claim.
We present a novel idea that combines the neural, statistical and external
features to provide an efficient solution to this problem. We compute the
neural embedding from the deep recurrent model, statistical features from the
weighted n-gram bag-of-words model and handcrafted external features with the
help of feature engineering heuristics. Finally, using deep neural layer all
the features are combined, thereby classifying the headline-body news pair as
agree, disagree, discuss, or unrelated. We compare our proposed technique with
the current state-of-the-art models on the fake news challenge dataset. Through
extensive experiments, we find that the proposed model outperforms all the
state-of-the-art techniques including the submissions to the fake news
challenge.

本文探讨假新闻鉴别的子任务，即立场检测，并提出了一种新的思路，将神经网络、统计特征和外部特征结合起来，通过深度神经网络将这些特征组合在一起，最终对新闻标题 - 正文进行分类，并在假新闻挑战数据集上进行了实验，结果表明该模型胜于现有的所有技术。