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Articles by Zhoujun Li
Computer Science > Computation and Language
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Computer Science > Artificial Intelligence
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2410.08583] Intent-Enhanced Data Augmentation for Sequential Recommendation
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Prompt-Enhanced Generation for Multimodal Open Question Answering
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Chenhao Cui SciProfiles Scilit Preprints.org Google Scholar 1 and Zhoujun Li SciProfiles Scilit Preprints.org Google Scholar 2,* 1 School of Cyber Science and Technology, Beihang University, Beijing 100191, China 2 School of Computer Science and Engineering, Beihang University, Beijing 100191, China * Author to whom correspondence should be addressed.
Incorporating Entity Type-Aware and Word-Word Relation-Aware Attention in Generative Named Entity Recognition
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
AAHEG: Automatic Advanced Heap Exploit Generation Based on Abstract Syntax Tree
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
[2208.11303] Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
arXiv:2208.11303 (cs) [Submitted on 24 Aug 2022 (v1), last revised 10 May 2023 (this version, v3)] Download a PDF of the paper titled Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization, by Chenhao Cui and 3 other authors Download PDF Submission history From: Xinnian Liang [ view email] [v1] Wed, 24 Aug 2022 05:18:23 UTC (26,838 KB) [v2] Wed, 5 Apr 2023 09:01:21 UTC (10,430 KB) [v3] Wed, 10 May 2023 15:54:12 UTC (10,430 KB) Bibliographic Tools...
Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures
2.4.2. Filter We introduce dependency parsing as a filtering method to filter out quantified candidates that are less relevant to a quantity mention. Different from MARVE [22] and other DP-based extraction methods, our method does not rely on complex dependency patterns. As the number of dependency types is large, and it becomes even larger with combinations, designing generalized matching patterns can be difficult.
SP-NLG: A Semantic-Parsing-Guided Natural Language Generation Framework
Abstract : We propose SP-NLG: A semantic-parsing-guided natural language generation framework for logical content generation with high fidelity. Prior studies adopt large pretrained language models and coarse-to-fine decoding techniques to generate text with logic; while achieving considerable results on automatic evaluation metrics, they still face challenges in terms of logical fidelity based on human evaluation.
APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection
Abstract : Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper.
Aye: A Trusted Forensic Method for Firmware Tampering Attacks
1. Introduction The Programmable Logic Controller (PLC) is widely used in Industrial Control Systems (ICSs), such as oil facilities, water supply, steel mills, and nuclear power plants, connecting the ICS’s network and physical space [1]. Initially, PLC was designed more concerned with usability than security. Almost all PLCs lack encryption, authorization, and authentication mechanisms, leading to vulnerable and weak security [2].
Multilevel Attention Networks and Policy Reinforcement Learning for Image Caption Generation
Big DataVol. 10, No. 6 Original ArticlesFree Access Abstract The analysis of large-scale multimodal data has become very popular recently. Image captioning, whose goal is to describe the content of image with natural language automatically, is an essential and challenging task in artificial intelligence. Commonly, most existing image caption methods utilize the mixture of Convolutional Neural Network and Recurrent Neural Network framework.
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[1710.06582] Learning Social Image Embedding with Deep Multimodal Attention Networks
Proceedings of Thematic Workshops of the 25th ACM Multimedia 2017
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