99 research outputs found
Cognitive Representation Learning of Self-Media Online Article Quality
The automatic quality assessment of self-media online articles is an urgent
and new issue, which is of great value to the online recommendation and search.
Different from traditional and well-formed articles, self-media online articles
are mainly created by users, which have the appearance characteristics of
different text levels and multi-modal hybrid editing, along with the potential
characteristics of diverse content, different styles, large semantic spans and
good interactive experience requirements. To solve these challenges, we
establish a joint model CoQAN in combination with the layout organization,
writing characteristics and text semantics, designing different representation
learning subnetworks, especially for the feature learning process and
interactive reading habits on mobile terminals. It is more consistent with the
cognitive style of expressing an expert's evaluation of articles. We have also
constructed a large scale real-world assessment dataset. Extensive experimental
results show that the proposed framework significantly outperforms
state-of-the-art methods, and effectively learns and integrates different
factors of the online article quality assessment.Comment: Accepted at the Proceedings of the 28th ACM International Conference
on Multimedi
Automated scholarly paper review: Technologies and challenges
Peer review is a widely accepted mechanism for research evaluation, playing a
pivotal role in scholarly publishing. However, criticisms have long been
leveled on this mechanism, mostly because of its inefficiency and subjectivity.
Recent years have seen the application of artificial intelligence (AI) in
assisting the peer review process. Nonetheless, with the involvement of humans,
such limitations remain inevitable. In this review paper, we propose the
concept and pipeline of automated scholarly paper review (ASPR) and review the
relevant literature and technologies of achieving a full-scale computerized
review process. On the basis of the review and discussion, we conclude that
there is already corresponding research and implementation at each stage of
ASPR. We further look into the challenges in ASPR with the existing
technologies. The major difficulties lie in imperfect document parsing and
representation, inadequate data, defective human-computer interaction and
flawed deep logical reasoning. Moreover, we discuss the possible moral &
ethical issues and point out the future directions of ASPR. In the foreseeable
future, ASPR and peer review will coexist in a reinforcing manner before ASPR
is able to fully undertake the reviewing workload from humans
SAINE: Scientific Annotation and Inference Engine of Scientific Research
We present SAINE, an Scientific Annotation and Inference ENgine based on a
set of standard open-source software, such as Label Studio and MLflow. We show
that our annotation engine can benefit the further development of a more
accurate classification. Based on our previous work on hierarchical discipline
classifications, we demonstrate its application using SAINE in understanding
the space for scholarly publications. The user study of our annotation results
shows that user input collected with the help of our system can help us better
understand the classification process. We believe that our work will help to
foster greater transparency and better understand scientific research. Our
annotation and inference engine can further support the downstream meta-science
projects. We welcome collaboration and feedback from the scientific community
on these projects. The demonstration video can be accessed from
https://youtu.be/yToO-G9YQK4. A live demo website is available at
https://app.heartex.com/user/signup/?token=e2435a2f97449fa1 upon free
registration.Comment: Under review in IJCNLP-AACL Demo 202
Unveiling the Sentinels: Assessing AI Performance in Cybersecurity Peer Review
Peer review is the method employed by the scientific community for evaluating
research advancements. In the field of cybersecurity, the practice of
double-blind peer review is the de-facto standard. This paper touches on the
holy grail of peer reviewing and aims to shed light on the performance of AI in
reviewing for academic security conferences. Specifically, we investigate the
predictability of reviewing outcomes by comparing the results obtained from
human reviewers and machine-learning models. To facilitate our study, we
construct a comprehensive dataset by collecting thousands of papers from
renowned computer science conferences and the arXiv preprint website. Based on
the collected data, we evaluate the prediction capabilities of ChatGPT and a
two-stage classification approach based on the Doc2Vec model with various
classifiers. Our experimental evaluation of review outcome prediction using the
Doc2Vec-based approach performs significantly better than the ChatGPT and
achieves an accuracy of over 90%. While analyzing the experimental results, we
identify the potential advantages and limitations of the tested ML models. We
explore areas within the paper-reviewing process that can benefit from
automated support approaches, while also recognizing the irreplaceable role of
human intellect in certain aspects that cannot be matched by state-of-the-art
AI techniques
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