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SSRN 2024年

作者： Liu, Haoyang Hu, Xue gang Bai, Shengxing Lin, Yaojin Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Anhui Hefei230009 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Zhangzhou363000 China

Hierarchical classification learning is an effective means of solving large-scale classification problems, which models classification problems at different levels of granularity according to a hierarchical structure. In hierarchical classification tasks, classification results at different levels of granularity are mutually beneficial, where coarse-grained results can reduce the number of fine-grained candidate categories, and fine-grained results can provide a basis for coarse-grained classification. However, for the final decision, the contribution of the granularity levels in the different hierarchies is different and unknown. Feedback from different granularity levels should be flexibly integrated. In addition, errors in hierarchical classification are not equivalent, and some errors can have serious results for practical applications. In order to address the above challenges, we propose a new hierarchical network to integrate multi-granularity knowledge for decision making. On the one hand, the network computes the decision scores of instances at each granularity level and learns a set of weights to combine all scores. Based on the learnable weights, the model can effectively utilize the multi-granularity classification results to obtain the final decision. On the other hand, we design local and global classification losses to guide model optimization, which allows the model to gain a hierarchical perspective and the ability to understand the degree of hierarchical misclassification. As a result, the extent to which the method results in misclassification consequences is limited. Experimental results on five benchmark datasets show that our method arises as a state-of-the-art hierarchical classification method. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

arXiv

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arXiv 2024年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Hong Kong University 999077 Hong Kong

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Generative Aspect Based Sentiment Analysis for Online Health Community with data Augmentation

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SSRN 2023年

作者： Han, Pu Ye, Dongyu Gu, Liang Chen, Wenqi Wei, Hongchen Li, Xiaoyan School of Management Nanjing University of Posts & Telecommunications Nanjing210003 China Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China School of Basic Medical Sciences Nanjing Medical University Nanjing210029 China

Accurate analysis of patients' feedback on various medical aspects is of great importance for improving the quality of healthcare services. In this paper, we address the task of extracting aspects and opinions by adopting a generative end-to-end deep learning model. And we address the challenge of limited high-quality labeled data in aspect-based sentiment analysis by introducing a data augmentation method. To evaluate the effectiveness of our proposed generative model and data augmentation technique in the healthcare domain, we collected reviews related to diagnosis and treatment from online health communities(OHC) and verified the effectiveness of the proposed model. The experimental results demonstrate the superiority of generative models in complex aspect-based sentiment analysis tasks. Furthermore, we find that our data augmentation method is effective in enhancing the experimental outcomes. Our work contributes to the advancement of natural language processing techniques in the healthcare domain, and it has the potential to improve the quality of healthcare services through more fine-grained analysis of patients' feedback. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics 10th

ARCH-COMP23 Category Report: Continuous and Hybrid Systems w...

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10th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2023

作者： Geretti, Luca dit Sandretto, Julien Alexandre Althoff, Matthias Benet, Luis Collins, Pieter Forets, Marcelo Ivanova, Elena Li, Yangge Mitra, Sayan Mitsch, Stefan Schilling, Christian Wetzlinger, Mark Zhuang, Daniel Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Universidad de la Repúblic Montevideo Uruguay Computer Science Department Carnegie Mellon University PittsburghPA United States Aalborg University Aalborg Denmark University of Illinois Urbana-Champaign United States

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2023. This year, 6 tools participated: Ariadne, CORA, DynIbex, JuliaReach, KeYmaera X and Verse (in alphabetic order). These tools are applied to solve reachability analysis problems on six benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2023, EasyChair. All rights reserved.

关键词： Hybrid systems

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MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

arXiv

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arXiv 2022年

作者： Liu, Kang Xue, Feng Guo, Dan Wu, Le Li, Shujie Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China Hefei University of Technology School of Software Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM). That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, multimodal entity graph collaborative filtering, short for MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals. Finally, the sentiment information fr

关键词： Sentiment analysis

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Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

arXiv

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arXiv 2023年

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Xiong, Hui Liu, Dexi Liu, Xiping Jiangxi Key Laboratory of Data and Knowledge Engineering China School of Information Management Jiangxi University of Finance and Economics Jiangxi Nanchang China College of Business Stony Brook University Stony BrookNY United States Guangdong Guangzhou China

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multichannel prediction module, we transform entity and multi-event extraction into a single task of predicting token-event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework. Copyright © 2023, The Authors. All rights reserved.

关键词： data structures

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Generative Aspect Based Sentiment Analysis for Online Health Community with data Augmentation

SSRN

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SSRN 2023年

Accurate analysis of patients' feedback on various medical aspects is of great importance for improving the quality of healthcare services. In this paper, we address the task of extracting aspects and opinions by adopting a generative end-to-end deep learning model. And we address the challenge of limited high-quality labeled data in aspect-based sentiment analysis by introducing a data augmentation method. To evaluate the effectiveness of our proposed generative model and data augmentation technique in the healthcare domain, we collected reviews related to diagnosis and treatment from online health communities(OHC) and verified the effectiveness of the proposed model. The experimental results demonstrate the superiority of generative models in complex aspect-based sentiment analysis tasks, showing a noteworthy nearly 13% increase in F1 score. Furthermore, we find that our data augmentation method is effective in enhancing the experimental outcomes. Our work contributes to the advancement of natural language processing techniques in the healthcare domain, and it has the potential to improve the quality of healthcare services through more fine-grained analysis of patients' feedback. Our code and dataset are available at https://***/woloc/OHC_*** now. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

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Emotion-Cause Pair Extraction Based on knowledge-Transfer 21

Emotion-Cause Pair Extraction Based on Knowledge-Transfer

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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Fengyuan, Zhao Dexi, Liu Qizhi, Wan Changxuan, Wan Xiping, Liu Guoqiong, Liao School of Information Management Jiangxi University of Finance and Economics Jiangxi Nanchang China Jiangxi University of Finance and Economics Key Laboratory of Data and Knowledge Engineering Jiangxi Nanchang China

The existing emotion cause pair extraction models do not improve the performance of emotion cause pair extraction by incorporating external knowledge. In this work, we propose an emotion-cause pair extraction model based on knowledge transfer (ECPE-KT), in which knowledge is utilized to obtain explicit knowledge encoding of text. Subsequently, the implicit knowledge encoding of clauses is obtained by the transfer of external sentiment classification corpus. Moreover, conduct filtering via adding the relative position and prediction probability to the representation of clause semantic features. Transformer with window mechanism is used to optimize the calculation pressure. Experimental results show that the proposed method outperforms the state-of-the-art method, i.e., ECPE-2D. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Extraction

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Access control of blockchain based on dual-policy attribute-based encryption 22

Access control of blockchain based on dual-policy attribute-...

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22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Han, Daojun Chen, Jinyu Zhang, Lei Shen, Yatian Wang, Xueheng Gao, Yihua Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781728176499

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through 'pseudo-Anonymity'. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center;instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally,

关键词： Blockchain

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