Deception, a prevalent aspect of human communication, has undergone a
significant transformation in the digital age. With the globalization of online
interactions, individuals are communicating in multiple languages and mixing
languages on social media, with varied data becoming available in each language
and dialect. At the same time, the techniques for detecting deception are
similar across the board. Recent studies have shown the possibility of the
existence of universal linguistic cues to deception across domains within the
English language; however, the existence of such cues in other languages
remains unknown. Furthermore, the practical task of deception detection in
low-resource languages is not a well-studied problem due to the lack of labeled
data. Another dimension of deception is multimodality. For example, a picture
with an altered caption in fake news or disinformation may exist. This paper
calls for a comprehensive investigation into the complexities of deceptive
language across linguistic boundaries and modalities within the realm of
computer security and natural language processing and the possibility of using
multilingual transformer models and labeled data in various languages to
universally address the task of deception detection.

通过使用多语言变形器模型和多语言标注数据，对跨语言和跨模态的虚假语言进行全面研究，以解决在计算机安全和自然语言处理领域的虚假检测任务。

多语种、多模态领域无关欺骗检测路线图

A Roadmap for Multilingual, Multimodal Domain Independent Deception  Detection

This research project aims to tackle the growing mental health challenges in
today's digital age. It employs a modified pre-trained BERT model to detect
depressive text within social media and users' web browsing data, achieving an
impressive 93% test accuracy. Simultaneously, the project aims to incorporate
physiological signals from wearable devices, such as smartwatches and EEG
sensors, to provide long-term tracking and prognosis of mood disorders and
emotional states. This comprehensive approach holds promise for enhancing early
detection of depression and advancing overall mental health outcomes.

本研究利用经过修改的预训练 BERT 模型检测社交媒体和用户的网络浏览数据中的抑郁文本，实现了令人印象深刻的 93% 测试准确率。同时，该项目旨在结合可穿戴设备（如智能手表和脑电传感器）的生理信号，提供情绪障碍和情绪状态的长期跟踪和预后，这种综合方法有望提高抑郁症的早期检测并促进整体心理健康结果。

AI 驅動的持續情緒監測器：主動式情緒跟蹤

Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion  Monitoring

The complexity of cultures in the modern world is now beyond human
comprehension. Cognitive sciences cast doubts on the traditional explanations
based on mental models. The core subjects in humanities may lose their
importance. Humanities have to adapt to the digital age. New, interdisciplinary
branches of humanities emerge. Instant access to information will be replaced
by instant access to knowledge. Understanding the cognitive limitations of
humans and the opportunities opened by the development of artificial
intelligence and interdisciplinary research necessary to address global
challenges is the key to the revitalization of humanities. Artificial
intelligence will radically change humanities, from art to political sciences
and philosophy, making these disciplines attractive to students and enabling
them to go beyond current limitations.

人文学科需要适应数字时代和人工智能的发展，以解决全球挑战并进行跨学科研究，从而重新振兴人文学科。

人工智能与人文学科的界限

Artificial intelligence and the limits of the humanities

Text is the main method of communicating information in the digital age.
Messages, blogs, news articles, reviews, and opinionated information abound on
the Internet. People commonly purchase products online and post their opinions
about purchased items. This feedback is displayed publicly to assist others
with their purchasing decisions, creating the need for a mechanism with which
to extract and summarize useful information for enhancing the decision-making
process. Our contribution is to improve the accuracy of extraction by combining
different techniques from three major areas, named Data Mining, Natural
Language Processing techniques and Ontologies. The proposed framework
sequentially mines products aspects and users opinions, groups representative
aspects by similarity, and generates an output summary. This paper focuses on
the task of extracting product aspects and users opinions by extracting all
possible aspects and opinions from reviews using natural language, ontology,
and frequent (tag) sets. The proposed framework, when compared with an existing
baseline model, yielded promising results.

本文研究如何使用数据挖掘、自然语言处理技术和本体论来提取产品方面和用户意见，并使用频繁（标记）集，讨论了提出的框架能够提取评论中所有可能的方面和意见，并通过相似性分组代表性方面，并生成输出摘要。该框架与现有基线模型相比，得到了有希望的结果。