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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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MULTIPLE HEADS ARE BETTER THAN ONE: MIXTURE OF MODALITY knowledge EXPERTS FOR ENTITY REPRESENTATION LEARNING

arXiv

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arXiv 2024年

作者： Zhang, Yichi Chen, Zhuo Guo, Lingbing Xu, Yajing Hu, Binbin Liu, Ziqi Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of the entities. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies, yet they overlook the utilization of multi-perspective features concealed within the modalities under diverse relational contexts. To address this issue, we introduce a novel framework with Mixture of Modality knowledge experts (MOMOK for short) to learn adaptive multi-modal entity representations for better MMKGC. We design relation-guided modality knowledge experts to acquire relation-aware modality embeddings and integrate the predictions from multi-modalities to achieve joint decisions. Additionally, we disentangle the experts by minimizing their mutual information. Experiments on four public MMKG benchmarks demonstrate the outstanding performance of MOMOK under complex scenarios. Our code and data are available at https://***/zjukg/MoMoK. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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International Conference on Network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined Networking (SDN) makes it a prime target for Distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors data models Ensemble learning Computer crime Software defined networking

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Model Predictive Control for Air Source Heat Pump Systems

SSRN

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SSRN 2024年

作者： Zhao, Ruochen Wei, Dong Jia, Yuchen Xu, Rongji Gao, Qiang Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing China Beijing University of Civil Engineering and Architecture School of Environment and Energy Engineering Beijing China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China

Air source heat pump (ASHP) systems exhibit non-linearity with significant inertia, posing challenges to ensure its operational robustness and enhance energy efficiency. This study proposed a non-linear model predictive control (MPC) strategy for an ASHP water heating system. The non-linear modeling methodology, based on thermodynamics and fluid dynamics, was developed. The sequential quadratic programming was employed to handle the non-convex optimization problem for receding horizon optimization. Furthermore, a sensor-less speed control approach was designed to address the issue of the absence of Hall sensors within the DC compressor. Experimental results demonstrated that the proposed strategy reduced heating time by 14.9% and 15.7% compared to PID and fuzzy PID control approaches, diminishing energy consumption by 14.6% and 9.1%, respectively. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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UGCM-LU: A Unified Stream and Batch Graph Computing Model with Local Update for Community Detection 19th

UGCM-LU: A Unified Stream and Batch Graph Computing Model w...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Li, Hong Wu, Ling Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Unified stream and batch computing (USBC) aims to incorporate stream and batch computation into a unified framework, thereby enabling the development of a one-stop solution for stream and batch data processing and enhancing the generalization of the framework. However, research on unified graph computing models (UGCMs) faces several challenges. First, existing UGCMs need to consider all graph information in the cache during the incremental update phase, thus leading to decreased execution efficiency. Second, existing UGCMs use fixed bytes to store nodes without considering the actual space occupied by nodes resulting in wasted memory when dealing with large graphs. This paper proposes a UGCM with Local Updates for community detection (UGCM-LU). We first implement a local update strategy to consider partial information of the graph to achieve incremental updates. Secondly, we also designed a byte-compression-based module to store graph data according to the space occupied by nodes. The experimental results show the effectiveness and efficiency of the model in real-world and artificial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lutetium alloys

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural Networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. This design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation

arXiv

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arXiv 2024年

Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models, resulting in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interactions. To tackle this shortfall, we introduce a novel framework MYGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities and enhance the MMKGC performance. Motivated by the tokenization technology, MYGO tokenizes multi-modal entity information as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MYGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance. Code and data can be found in https://***/zjukg/MyGO. Copyright © 2024, The Authors. All rights reserved.

关键词： knowledge graph

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Network Security Situation Prediction Implemented by Attention and BiLSTM

Network Security Situation Prediction Implemented by Attenti...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Zhao, Dongmei Wu, Yaxing Li, Qingru Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Shijiazhuang China Hebei Normal University College of Computer and Cyberspce Security Shijiazhuang China Hebei Normal University Hebei Supply Chain Big Data Analysis Data Security Engineering Research Center Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

With the increasing diversification and complexity of network security attacks, it is becoming more and more difficult to predict the network situation. In order to improve the effect of situation prediction, this paper constructs a network security situation prediction model for a Improved Particle Swarm Optimization and Attention fusion Bidirectional Long Short-Term Memory (IPSO-ABiLSTM). First, there is no real situation value for the UNSW-NB15 data set, and a situation value is generated based on the impact of the attack. Secondly, the particle swarm algorithm is improved. The IPSO algorithm makes the algorithm's global and local search capabilities more balanced and faster to converge. Finally, optimizing the hyperparameters of the BiLSTM network fused with the attention mechanism to obtain the final model, and combined with PSO-BiLSTM network, PSO-LSTM network, BiLSTM model for performance comparison. The experimental results show that the IPSO-ABiLSTM in this paper has a fitting degree of up to 0.9922, and the error value is relatively smaller, which verifies the effectiveness of the model proposed in this paper in the network security situation prediction problem. © 2022 IEEE.

关键词： Long short-term memory

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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SCRN: A Spectrogram Convolutional Recurrent Network for AoA Estimation Using Bluetooth 5

SCRN: A Spectrogram Convolutional Recurrent Network for AoA ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wentao Shi Baoqi Huang Bing Jia Engineering Research Center of Ecological Big Data Ministry of Education China Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

Bluetooth 5 employs Constant Tone Extension (CTE) signals to simulate arrival times of a common signal at multiple antennas, but incurs limited accuracy due to inevitable frequency offsets caused by Gaussian frequency shift keying (GFSK) and time offsets caused by antenna switching. To address this issue, this paper proposes SCRN, an efficient Bluetooth 5 Angle of Arrival(AoA) estimation scheme that combines spectrogram analysis with deep learning co-design. SCRN leverages neural networks to generate a super-resolution spectrogram, effectively eliminating frequency offsets and recovering the true underlying frequency. Subsequently, it utilizes a lightweight convolutional recurrent network to encode the input features into a high-dimensional latent space, so as to alleviate the effect of time offsets. Through extensive experiments conducted in practical indoor settings, the proposed scheme achieves an AoA estimation error of only 1.3 degrees, surpassing traditional algorithms by over 90%.

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