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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhou, Baohang Zhang, Ying Song, Kehui Guo, Wenya Zhao, Guoqing Wang, Hongbin Yuan, Xiaojie College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Mashang Consumer Finanace Co Ltd

Multimodal named entity recognition (MNER) on social media is a challenging task which aims to extract named entities in free text and incorporate images to classify them into user-defined types. The existing semi-supervised named entity recognition methods focus on the text modal and are utilized to reduce labeling costs in traditional NER. However, the previous methods are not efficient for semi-supervised MNER. Because the MNER task is defined to combine the text information with image one and needs to consider the mismatch between the posted text and image. To fuse the text and image features for MNER effectively under semi-supervised setting, we propose a novel span-based multimodal variational autoencoder (SMVAE) model for semi-supervised MNER. The proposed method exploits modal-specific VAEs to model text and image latent features, and utilizes product-of-experts to acquire multimodal features. In our approach, the implicit relations between labels and multimodal features are modeled by multimodal VAE. Thus, the useful information of unlabeled data can be exploited in our method under semi-supervised setting. Experimental results on two benchmark datasets demonstrate that our approach not only outperforms baselines under supervised setting, but also improves MNER performance with less labeled data than existing semi-supervised methods. © 2022 Association for Computational Linguistics.

关键词： Benchmarking

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PM2F2N: Patient Multi-view Multi-modal Feature Fusion networks for Clinical Outcome Prediction

PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhang, Ying Zhou, Baohang Song, Kehui Sui, Xuhui Zhao, Guoqing Jiang, Ning Yuan, Xiaojie College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Mashang Consumer Finanace Co Ltd

Clinical outcome prediction is critical to the condition prediction of patients and management of hospital capacities. There are two kinds of medical data, including time series signals recorded by various devices and clinical notes in electronic health records (EHR), which are used for two common prediction targets: mortality and length of stay. Traditional methods focused on utilizing time series data but ignored clinical notes. With the development of deep learning, natural language processing (NLP) and multi-modal learning methods are exploited to jointly model the time series and clinical notes with different modals. However, the existing methods failed to fuse the multi-modal features of patients from different views. Therefore, we propose the patient multi-view multi-modal feature fusion networks for clinical outcome prediction. Firstly, from patient inner view, we propose to utilize the co-attention module to enhance the fine-grained feature interaction between time series and clinical notes from each patient. Secondly, the patient outer view is the correlation between patients, which can be reflected by the structural knowledge in clinical notes. We exploit the structural information extracted from clinical notes to construct the patient correlation graph, and fuse patients' multi-modal features by graph neural networks (GNN). The experimental results on MIMIC-III benchmark demonstrate the superiority of our method. © 2022 Association for Computational Linguistics.

关键词： Time series

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Exploration of Teaching Method of Students’ Independent Proposition

Exploration of Teaching Method of Students’ Independent Pro...

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International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Zhang, Jin Wang, Lizhi Quan, Weihong Li, Haoran Gong, Xiaoli College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9789811581540

Aiming at obsolescence and deficiencies of the question bank in the current teaching of computer major courses, we propose a teaching method of student independent proposition, which can not only enrich the question bank as well as ensure the novelty of the exercises, but also effectively obtain feedback data of teaching quality based on the answers to exercises. In this paper, we first discuss the teaching significance of independent proposition and some key issues such as quality management encountered in practice. Then, we propose solutions to the problems of correctness checking and difficulty grading based on some natural language processing and deep learning technologies. Finally, to prove the effectiveness of our approach, we introduce the specific application in teaching practice, including the construction and use of the online education system based on the artificially constructed question bank. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training

Integrating Structural Semantic Knowledge for Enhanced Infor...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yi, Xiaoyang Bao, Yuru Zhang, Jian Qin, Yifang Lin, Faxin College of Cyber Science Nankai University China College of Computer Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China

ISBN: (纸本)9798891761643

Information Extraction (IE), aiming to extract structured information from unstructured natural language texts, can significantly benefit from pre-trained language models. However, existing pre-training methods solely focus on exploiting the textual knowledge, relying extensively on annotated large-scale datasets, which is labor-intensive and thus limits the scalability and versatility of the resulting models. To address these issues, we propose SKIE, a novel pre-training framework tailored for IE that integrates structural semantic knowledge via contrastive learning, effectively alleviating the annotation burden. Specifically, SKIE utilizes Abstract Meaning Representation (AMR) as a low-cost supervision source to boost model performance without human intervention. By enhancing the topology of AMR graphs, SKIE derives high-quality cohesive subgraphs as additional training samples, providing diverse multi-level structural semantic knowledge. Furthermore, SKIE refines the graph encoder to better capture cohesive information and edge relation information, thereby improving the pre-training efficacy. Extensive experimental results demonstrate that SKIE outperforms state-of-the-art baselines across multiple IE tasks and showcases exceptional performance in few-shot and zero-shot settings. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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There are finitely many 5-vertex-critical (P6, bull)-free graphs

arXiv

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arXiv 2025年

作者： Ju, Yiao Jooken, Jorik Goedgebeur, Jan Huang, Shenwei College of Computer Science Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium School of Mathematical Sciences and LPMC Nankai University Tianjin300071 China

In this paper, we are interested in 4-colouring algorithms for graphs that do not contain an induced path on 6 vertices nor an induced bull, i.e., the graph with vertex set {v1, v2, v3, v4, v5} and edge set {v1v2, v2v3, v3v4, v2v5, v3v5}. Such graphs are referred to as (P6, bull)-free graphs. A graph G is k-vertex-critical if χ(G) = k, and every proper induced subgraph H of G has χ(H) 6, bull)-free graphs and show that there are only finitely many such graphs, thereby answering a question of Maffray and Pastor. A direct corollary of this is that there exists a polynomial-time algorithm to decide if a (P6, bull)-free graph is 4-colourable such that this algorithm can also provide a certificate that can be verified in polynomial time and serves as a proof of 4-colourability or non-4-colourability. © 2025, CC BY.

关键词： Polynomial approximation

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Design and Implementation of Rumor Governance System Based on Blockchain 2

Design and Implementation of Rumor Governance System Based o...

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2nd International Conference on Computer Science and Blockchain, CCSB 2022

作者： Li, Yutong Song, Jiazhen Wang, Jingrong Lu, Haori Nie, Peng College of Cyber Science Nankai University Tianjin300350 China Nankai University College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

ISBN: (纸本)9798350331882

Nowadays, the network rumor is constantly breeding and spreading. It becomes a big difficulty to trace the source and refute the rumors with high efficiency. For this, we designed and implemented an intelligent rumor detection, source tracing and accurate rumor refuting system. This system has three main functions. First, the system provides credible evidence for the tracing and accountability of rumors with the immutable nature of blockchain. Second, we propose a new intelligent rumor detection model that does not involve manual work, that is, by looking for opposite semantic pairs. Third, a new accurate rumor refuting scheme is proposed, which can help break through the information cocoons. The system provides a standardized and effective management platform for the collaborative governance of online rumors in the new era and the improvement of the environment for online public opinion. It also facilitates the development of the Clear Action and the crackdown on online rumors. © 2022 IEEE.

关键词： Smart contract

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Exploration on the key Issues of the Teaching Method of Student Independent Proposition

Exploration on the Key Issues of the Teaching Method of Stud...

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International Conference on Guidance, Navigation and Control, ICGNC 2020

作者： Zhang, Jin Quan, Weihong Li, Haoran Wang, Lizhi Gong, Xiaoli Wu, Qian College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Tianjin University of Science and Technology Tianjin China

ISBN: (纸本)9789811581540

In order to figure out the solutions to difficulties facing the university online education platform in the expansion of the question bank, this paper proposes a teaching method of student independent proposition that allows students to create exercises independently based on the content of the textbook. In view of the high cost of manual review of these student-created exercises, natural language processing and deep learning technologies are used to achieve automatic correctness checking. For the key issues encountered in the actual construction of the independent proposition system, a feasible solution is proposed. The research is of great significance to the construction and subsequent application of the question bank of the online education platform of universities. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Design and Implementation of Cyber Space Threat Detection System Based on User Behavioral Logs 3

Design and Implementation of Cyber Space Threat Detection Sy...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Wang, Wenhao Li, Hao Nie, Peng College of Cyber Science Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Science and Technology on Communication Networks Laboratory Shijiazhuang China College of Cyber Science Nankai University Tianjin China

ISBN: (纸本)9798350359961

In contemporary society, the role of networks in daily work and business activities is increasingly prominent. The enterprise intranet, serving as a crucial bridge for maintaining work stability, asset security, and business continuity, has garnered significant attention from both academia and industry in the field of intranet security detection. This study is based on the Log2Vec algorithm and involves the design and implementation of a Cyber Space Threat Detection System. The system, facilitated by the collaborative efforts of three modules-log ingestion, graph encoding, and security detection-effectively showcases user behavior, encodes logs into a heterogeneous graph, transforms the graph into vectors, and conducts cluster analysis to detect abnormal user behavior. Through in-depth research and systematic design, this study aims to provide enterprises with a more advanced and comprehensive intranet security detection solution to cope with evolving cybersecurity threats. © 2023 IEEE.

关键词： Cybersecurity Heterogeneous Graph Log2Vec

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Leaky Autofill: An Empirical Study on the Privacy Threat of Password Managers’ Autofill Functionality 40

Leaky Autofill: An Empirical Study on the Privacy Threat of ...

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40th Annual Computer security Applications Conference, ACSAC 2024

作者： Fu, Yanduo Wang, Ding College of Cyber Science Nankai University Tianjin300350 China Ministry of Education Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9798331520885

Password managers (PMs) provide users with convenient and robust functionalities to manage their credentials, highly recommended by security experts and major standard bodies. One of the most popular features is the autofill functionality, with which users need a single click or a few clicks to fill in every field in web forms, facilitating the process of completing web forms. However, such indiscriminate autofill brings severe privacy threats. PMs may inadvertently fill data into wrong fields in web forms, even hidden fields, potentially leading to privacy leaks and credential theft. In this paper, we conduct an empirical study evaluating the effectiveness of 30 popular PMs in identifying and handling hidden fields. We focus on the privacy threats posed by the autofill functionality, which fills data into hidden fields. We develop a semi-automated autofill testing tool and explore whether PMs autofill sensitive data into hidden fields across 15 concealment techniques and three web forms, including personal information, credit card, and login forms. Experimental results reveal that every PM autofills data into hidden fields in at least one web form, with an overall filled probability of 58.7% in 1032 scenarios. Further analysis reveals that login forms are the most vulnerable, with a 65.7% probability of hidden fields autofill. Hidden fields concealed by clip-path and content-visibility are filled with passwords by all PMs. Besides, built-in-browser PMs exhibit a 4.07 times higher likelihood of filling data into hidden fields than separately-installed PMs. Even more concerning, built-in-browser PMs, except Safari, autofill passwords into hidden fields under any concealment technique. 37.7% of autofill scenarios with insufficient user interaction pose heightened privacy threats, as users are unaware of autofill content. These privacy threats have been confirmed by popular PMs like LastPass. To mitigate the threats brought by the autofill functionality, we present two

关键词： Visual languages

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Design and Implementation of Analyzer Management System Based on Elasticsearch 19th

Design and Implementation of Analyzer Management System Ba...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Sun, Jingqi Nie, Peng Xu, Licheng Zhang, Haiwei College of Cyber Science Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9783031203084

Elasticsearch is one of the most popular full-text search and analytics engine. It can store, search, and analyze big volumes of data in near real time. Before searching, Elasticsearch will build multiple inverted indexes for the data. The analyzer plays a crucial role in this process. An appropriate analyzer can segment text into semantically meaningful words, which can significantly improve the query accuracy. However, the default analyzer has limited performance in the Chinese context. Existing methods generally replace the default analyzer with manual configuration to optimize the query effect. The cost of service downtime caused by manually updating analyzers is often unacceptable in production environments. Based on Elasticsearch’s Restful-API, we have implemented a framework for dynamic configuration of analyzers in a cluster environment. The framework supports common Chinese analyzers and provides a visual interface. Experiments show that the framework proposed in this paper reduces the update and maintenance time cost of the analyzer in the online environment by 94% compared to manual update. At the same time, compared with the default analyzer configuration of Elasticsearch, the accuracy of the system based on this framework is improved by 30%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information management

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