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10th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2024

作者： Ji, Lu Chen, Lei Li, Jing Wei, Zhongyu Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China School of Data Science Fudan University China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Intelligent and Complex Systems Fudan University China

ISBN: (纸本)9798891761551

Vast amount of online conversations are produced on a daily basis, resulting in a pressing need to automatic conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a neural model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts. © 2024 Association for Computational Linguistics

关键词： Computational linguistics

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Asymptotic properties of generalized maximum likelihood hyper-parameter estimator for regularized system identification 63

Asymptotic properties of generalized maximum likelihood hype...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Meng Chen, Tianshi Mu, Biqiang The Chinese University of Hong Kong School of Data Science Shenzhen Research Institute of Big Data Shenzhen518172 China Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Key Laboratory of Systems and Control Beijing100190 China

ISBN: (纸本)9798350316339

Regularized system identification is one of the major advances in the field of system identification in the last decade. One key issue is the hyper-parameter estimation, for which the generalized maximum likelihood (GML) estimator is a popular one closely related to the empirical Bayes (EB) method. Considering the rich theoretical results on the EB estimator, the asymptotic properties of the GML estimator has not been studied before and is critical for understanding its efficacy when the sample size is large. In this paper, we investigate the asymptotic properties of the GML estimator and show that the GML estimator is asymptotically equivalent to the EB estimator. Furthermore, Monte-Carlo simulations verify their asymptotic equivalence and also indicates the GML estimator outperform the EB estimator for small sample sizes. © 2024 IEEE.

关键词： Decentralized systems

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Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports

Improving VTE Identification through Adaptive NLP Model Sele...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Deng, Jamie Wu, Yusen Hayssen, Hilary Englum, Brain Kankaria, Aman Mayorga-Carlin, Minerva Sahoo, Shalini Sorkin, John Lal, Brajesh Yesha, Yelena Nguyen, Phuong University of Miami Frost Institute for Data Science & Computing FL United States University of Maryland School of Medicine MD United States

ISBN: (纸本)9798350337488

Rapid and accurate identification of Venous thromboembolism (VTE), a severe cardiovascular condition including deep vein thrombosis (DVT) and pulmonary embolism (PE), is important for effective treatment. Leveraging Natural Language Processing (NLP) on radiology reports, automated methods have shown promising advancements in identifying VTE events from retrospective data cohorts or aiding clinical experts in identifying VTE events from radiology reports. However, effectively training Deep Learning (DL) and the NLP models is challenging due to limited labeled medical text data, the complexity and heterogeneity of radiology reports, and data imbalance. This study proposes novel method combinations of DL methods, along with data augmentation, adaptive pre-trained NLP model selection, and a clinical expert NLP rule-based classifier, to improve the accuracy of VTE identification in unstructured (free-text) radiology reports. Our experimental results demonstrate the model's efficacy, achieving an impressive 97% accuracy and 97% F1 score in predicting DVT, and an outstanding 98.3% accuracy and 98.4% F1 score in predicting PE. These findings emphasize the model's robustness and its potential to significantly contribute to VTE research. © 2023 IEEE.

关键词： BERT Bi-LSTM Deep Learning NLP Transfer Learning VTE

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M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

M3Hop-CoT: Misogynous Meme Identification with Multimodal Mu...

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

作者： Kumari, Gitanjali Jain, Kirtan Ekbal, Asif Department of Computer Science and Engineering India Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3HopCoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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Identifying the best common factor model via exploratory eactor analysis

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Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 24-33页

作者： HE Bao-hua TANG Rui TAGN Qi-yi School of Big Data Fuzhou University of International Studies and TradeFuzhou 350202China School of Mathematics Sun Yat-Sen UniversityGuangzhou 510275China Department of Computer Science University of BathUK Institute of Insect Science Zhejiang UniversityHangzhou 310058China.

Currently,there is no solid criterion for judging the quality of the estimators in factor *** paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor *** proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor *** are presented to demonstrate how the method is implemented and to verify its effectiveness.

关键词： factor analysis Shapiro-Wilk normality residuals

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Performance Analysis of GCN, GNN, and GAT Models with Differentiable Pooling for Detection of Fake News 3

Performance Analysis of GCN, GNN, and GAT Models with Differ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Gupta, Prisha Gupta, P.K. Amrita School of Artificial Intelligence Amrita Vishwa Vidhyapeetham Coimbatore India School of Computing Mohan Babu University Department of Data Science Tirupati India

ISBN: (纸本)9798331518592

In the era of social networks, the spread of fake news has become a critical issue that poses significant challenges to society. It is often difficult to distinguish between real news and fake news due to the sophisticated methods used to create misleading information. This paper presents a comprehensive approach to detecting fake news. The study conducts a systematic review of the performance of the Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Neural Networks using DiffPool (GNN-DP) in Twitter data. It takes into account the limitations identified in the previous studies and fine-tunes the parameters to obtain better results. Performance evaluation of the proposed model was done using metrics such as accuracy, F1 score, and area under the curve (AUC). In the results obtained, it is found that GCN, with its dynamic attention mechanism, outperforms GAT and GNN-DP by effectively highlighting the most relevant information within the graph. Next, we discuss the practical implications of fake news detection and the GCN model. Finally, we present the future scope in this area for further research. © 2024 IEEE.

关键词： Graph neural networks

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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MusCare+: Muscle Monitoring for Anomalies

MusCare+: Muscle Monitoring for Anomalies

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2022 IEEE World AI IoT Congress, AIIoT 2022

作者： Foley, Nicholas Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9781665484534

Muscles are an essential part of everyday life and any damage or illness that affects them can cause massive problems. Patients who are diagnosed with muscle injuries and illnesses largely remain unmonitored, even though a few appointments they have with doctors annually. Moving from unmonitored to constant monitoring can not only paint a better picture of how a muscle condition is progressing, but it also can inform medical professionals if their treatment regimen is actually working. In this paper, we propose a new system that can monitor muscle health of a patient and predict the muscle conditions. This system mainly focuses on the shoulder but could be expanded to other areas of the body. By utilizing the strength of machine learning and the Android platform, we created a platform that can monitor muscle health quickly and easily. The current prototype system is not only able to display live data gathered from an EMG sensor, but it can also predict whether the muscle is currently flexed or relaxed. Although there is a limitation in current prototype system, a more robust machine learning algorithm could be trained to give a wide array of muscle health predictions. copy;2022 IEEE. © 2022 IEEE.

关键词： Forecasting

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A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

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IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National institute of Standards and Technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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