Holographic Intellectual Voice Assistant (HIVA) aims to facilitate human
computer interaction using audiovisual effects and 3D avatar. HIVA provides
complete information about the university, including requests of various
nature: admission, study issues, fees, departments, university structure and
history, canteen, human resources, library, student life and events,
information about the country and the city, etc. There are other ways for
receiving the data listed above: the university's official website and other
supporting apps, HEI (Higher Education Institution) official social media,
directly asking the HEI staff, and other channels. However, HIVA provides the
unique experience of "face-to-face" interaction with an animated 3D mascot,
helping to get a sense of 'real-life' communication. The system includes many
sub-modules and connects a family of applications such as mobile applications,
Telegram chatbot, suggestion categorization, and entertainment services. The
Voice assistant uses Russian language NLP models and tools, which are pipelined
for the best user experience.

本研究提出了全新的 Holographic Intellectual Voice Assistant（HIVA) 系统，以其三维虚拟形象、语音交互等方式，为人们提供个性化、真人化的大学服务，其自然语言处理技术和模型，进一步提升了用户体验。

全息智能语音助手 (HIVA)

HIVA: Holographic Intellectual Voice Assistant

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。