Many software systems, such as online social networks enable users to share
information about themselves. While the action of sharing is simple, it
requires an elaborate thought process on privacy: what to share, with whom to
share, and for what purposes. Thinking about these for each piece of content to
be shared is tedious. Recent approaches to tackle this problem build personal
assistants that can help users by learning what is private over time and
recommending privacy labels such as private or public to individual content
that a user considers sharing. However, privacy is inherently ambiguous and
highly personal. Existing approaches to recommend privacy decisions do not
address these aspects of privacy sufficiently. Ideally, a personal assistant
should be able to adjust its recommendation based on a given user, considering
that user's privacy understanding. Moreover, the personal assistant should be
able to assess when its recommendation would be uncertain and let the user make
the decision on her own. Accordingly, this paper proposes a personal assistant
that uses evidential deep learning to classify content based on its privacy
label. An important characteristic of the personal assistant is that it can
model its uncertainty in its decisions explicitly, determine that it does not
know the answer, and delegate from making a recommendation when its uncertainty
is high. By factoring in the user's own understanding of privacy, such as risk
factors or own labels, the personal assistant can personalize its
recommendations per user. We evaluate our proposed personal assistant using a
well-known data set. Our results show that our personal assistant can
accurately identify uncertain cases, personalize them to its user's needs, and
thus helps users preserve their privacy well.

通过使用显式地建模不确定性来代表隐私问题，运用证据深度学习自适应分类，结合用户自身对隐私的理解和标签，本文提出了一种个人助手，其能够精确识别不确定的情况，并根据用户的需求提供个性化推荐，从而有助于用户更好地保护自己的隐私。

面向不确定性的个人助手：帮助制定个性化隐私决策

Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions

Temporal Action Localization (TAL) has experienced remarkable success under
the supervised learning paradigm. However, existing TAL methods are rooted in
the closed set assumption, which cannot handle the inevitable unknown actions
in open-world scenarios. In this paper, we, for the first time, step toward the
Open Set TAL (OSTAL) problem and propose a general framework OpenTAL based on
Evidential Deep Learning (EDL). Specifically, the OpenTAL consists of
uncertainty-aware action classification, actionness prediction, and temporal
location regression. With the proposed importance-balanced EDL method,
classification uncertainty is learned by collecting categorical evidence
majorly from important samples. To distinguish the unknown actions from
background video frames, the actionness is learned by the positive-unlabeled
learning. The classification uncertainty is further calibrated by leveraging
the guidance from the temporal localization quality. The OpenTAL is general to
enable existing TAL models for open set scenarios, and experimental results on
THUMOS14 and ActivityNet1.3 benchmarks show the effectiveness of our method.
The code and pre-trained models are released at
this https URL

本文提出了 OpenTAL 框架，以 Evidential Deep Learning 为基础解决了开放场景下不可避免的未知动作问题，该框架由三个方面组成：具有不确定性的动作分类、动作性预测和时间位置回归，实验结果证明了该方法的有效性。

OpenTAL: 面向开放集合的时序动作定位

OpenTAL: Towards Open Set Temporal Action Localization

A probabilistic classifier with reliable predictive uncertainties i) fits
successfully to the target domain data, ii) provides calibrated class
probabilities in difficult regions of the target domain (e.g.\ class overlap),
and iii) accurately identifies queries coming out of the target domain and
rejects them. We introduce an original combination of Evidential Deep Learning,
Neural Processes, and Neural Turing Machines capable of providing all three
essential properties mentioned above for total uncertainty quantification. We
observe our method on five classification tasks to be the only one that can
excel all three aspects of total calibration with a single standalone
predictor. Our unified solution delivers an implementation-friendly and compute
efficient recipe for safety clearance and provides intellectual economy to an
investigation of algorithmic roots of epistemic awareness in deep neural nets.

本文介绍了一种结合了 Evidential Deep Learning、Neural Processes 和 Neural Turing Machines 的概率分类器应用，本分类器能够对目标域数据进行成功拟合并在目标域的困难区域（例如类重叠）提供经过校准的分类概率。同时，在所有五个分类任务中，我们的方法都是唯一能够在单个独立的预测器中胜任三个方面的全面校准的。此方法为深度神经网络中认知感知的研究提供了实现友好的计算有效解决方案，为算法根源的调查提供了智力经济性。