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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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GraphConvNet: A Dual Network Utilizing Local Features Coupled with Structural Information for Predicting Knee Osteoarthritis

GraphConvNet: A Dual Network Utilizing Local Features Couple...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pengju Tang Dawei Dai Kai Zou Xionghui Yang Guoyin Wang Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor’s clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet.

关键词： Representation learning Visualization Decision making Medical services Feature extraction Graph neural networks Convolutional neural networks Osteoarthritis Diagnostic radiography Diseases

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FBOD: An Outlier Detection Algorithm based on data Features Suitable for Processing Large-scale datasets on Distributed Platforms 10

FBOD: An Outlier Detection Algorithm based on Data Features ...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Song, Hu Cheng, Xinyun Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

The primary objective of outlier detection is to identify values that are significantly different from other data in the dataset. However, most of the current algorithms are effective for small-scale data and their performance is highly dependent on the choice of parameters. To this, the paper proposes an innovative outlier detection algorithm, named FBOD, which is based on data features. Specifically, FBOD utilizes the number of combinations of attribute values in each tuple across the dataset as a key feature of the tuple. This feature reflects the degree of differentiation of each data tuple from the overall dataset. By identifying data features that are significantly different from objects in the entire dataset, FBOD can accurately determine outliers. Based on the knowledge of attribute importance in Rough Set Theory, FBOD can efficiently reduce the data dimensionality, so high-dimensional datasets can be handled by FBOD. Furthermore, this paper employs a distributed parallelism approach in the algorithm. The performance of FBOD is evaluated on several datasets, including standard datasets and artificial datasets, and is compared with common outlier detection algorithms. Experimental results indicate that, compared to existing typical algorithms, FBOD achieves higher outlier detection accuracy without relying on a prior knowledge of datasets or parameter selection. It also proves the suitability of FBOD for large-scale dataset processing on distributed platforms. © 2024 IEEE.

关键词： Anomaly detection

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BSRNet: Blind Super-Resolution of Low-Dose CT Images Based on Adaptive Routing

BSRNet: Blind Super-Resolution of Low-Dose CT Images Based o...

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Computer science, Engineering, and Education (CSEE), International Conference on

作者： Menglei Gao Peng Wu Shandong Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

ISBN: (数字)9798331505165

ISBN: (纸本)9798331505172

Conventional super-resolution (SR) methods for computed tomography (CT) typically rely on a fixed degradation model, such as downsampling and noise. However, the actual degradation process is more complex, making it difficult to accurately recover fine pathological details. To address this issue, we propose a blind super-resolution (BSR) method, named Blind Super-Resolution of Low-Dose CT Images Based on Adaptive Routing (BSRNet). BSRNet dynamically selects expert sub-networks in both spatial and channel dimensions according to the degradation level of the input image, effectively reducing noise and restoring details. We introduce a Depth-Separable Gated Feedforward Network (DGFN) that enhances the capture of both global and local features, improving sensitivity to lesion details. Additionally, we propose two fusion modules: the Dynamic Feature Fusion Module (DFFM) and the Adaptive Feature Fusion Module (AFFM). The DFFM replaces traditional U-Net skip connections to enable adaptive fusion of encoded and decoded features, while the AFFM dynamically adjusts the fusion ratio between shallow and deep features. Extensive experiments demonstrate that our approach outperforms existing SR methods across various datasets.

关键词： Degradation Adaptive systems Sensitivity Computed tomography Superresolution Noise Routing Transformers Image restoration Lesions

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Research on Backward Cloud Transformation Algorithm Based on Adding Cloud Drops 9

Research on Backward Cloud Transformation Algorithm Based on...

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Kong, Lingzhuo School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ning Xia Province Yinchuan750021 China

ISBN: (纸本)9781643684703

As a tool for studying uncertain information, cloud model is of great significance in uncertain artificial intelligence and data mining. Among them, the backward cloud transformation method is one of the important algorithms of cloud model, which can realize the conversion from quantitative data to qualitative concepts. In this paper, a dynamic incremental backward cloud transformation algorithm is proposed to solve the problem that the estimated value of hyper entropy is imaginary when the backward cloud transformation method is used to calculate hyper entropy. First, according to the formation characteristics of cloud drops, the generated samples and the original samples are fused as new samples by dynamically and randomly generating samples, and then the hyper entropy is estimated until the estimated value of hyper entropy is real. Secondly, the stability and convergence of the algorithm proposed in this paper are analyzed through simulation experiments. The experimental results show that the new dynamic incremental backward cloud transformation algorithm solves the problem that the hyper entropy estimation value is imaginary while the estimation error is small and the stability is good. Finally, the algorithm is applied to brain CT segmentation, and the results show that the algorithm is effective and practical. © 2023 The authors and IOS Press.

关键词： data mining

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intelligent SDN Routing: A Threshold-Based and LSTM-Enhanced Deep Q-Network Routing Algorithm

Intelligent SDN Routing: A Threshold-Based and LSTM-Enhanced...

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2023 International Conference on Electronics, Computers and Communication Technology, CECCT 2023

作者： Liu, Bo Yang, Bo Sun, Runyuan Liang, Zhifeng Sun, Zhenxiang Li, Zhenyu School of Information Science and Engineering University of Jinan 250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan 250022 China Quancheng Laboratory China and School of Information Science and Engineering University of Jinan 250022 China

ISBN: (纸本)9798400707629

With the rapid development of Internet technology and the diversification of new network applications and services, traditional routing protocols make decisions based on limited information, which leads to untimely responses to changing network demands and limitations in meeting the quality of service requirements of applications. As a new type of network architecture, a Software-Defined Network (SDN) can obtain a global view of the network, realize more granular network monitoring, and flexible deployment of network functions. With the rise of artificial intelligence, SDN routing algorithms based on machine learning gradually show potential for development, however, such algorithms usually can only deal with discrete low-dimensional action spaces and rely on in-network data for training, which is ineffective for dynamic network environments. Therefore, this paper proposes a framework of intelligent SDN Routing (ISR) based on SDN and designs a threshold-based LSTM-enhanced Deep Q-Network Routing Algorithm (TL- DQN-RA ) applied in SDN, TL-DQN-RA adds Long Short-Term Memory (LSTM) to Deep Q-Network (DQN) and sets the experience playback pool threshold to accelerate the convergence of the algorithm, so that the network can learn in real-time and automatically adjust the routing strategy to cope with the changing network demands. Experiments show that TL-DQN-RA significantly outperforms existing routing solutions in terms of key performance, and the ISR framework provides strong support for network performance improvement. © 2023 ACM.

关键词： Reinforcement learning

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Physically Secure and Privacy-Preserving Authentication Scheme for VANET

Physically Secure and Privacy-Preserving Authentication Sche...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Chen, Dongmei Liang, Yangfan Li, Lihui Liu, Yining School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

ISBN: (纸本)9798331507886

The communication between vehicles and other entities occurs over open wireless networks, making them susceptible to various security attacks In Vehicular Ad Hoc Network (VANET). Consequently, many researchers have proposed numerous data transmission and key exchange protocols to ensure secure communication within VANET. The keys and sensitive information involved in these protocols need to be stored long-Term in the tamper-resistant devices of vehicles. However, due to the limited resources of these devices, many encryption schemes are difficult to embed. Even if effective encryption schemes can be embedded in VANET, the long-Term storage of data in vehicles still carries a significant risk of being attacked. To tackle this problem, we propose an authentication protocol designed for VANET that emphasizes both physical security and privacy protection. The protocol primarily leverages the properties of Physical Unclonable Functions to achieve enhanced security. This approach eliminates the need for vehicles to store keys and sensitive messages long-Term, reducing the storage burden on vehicles while enhancing the system's security and efficiency. Detailed security analyses demonstrate that our proposed scheme achieves the goals of physical security and privacy protection. A comparison with similar approaches demonstrates that our solution outperforms them in both time efficiency and communication overhead. © 2024 IEEE.

关键词： Authentication Protocol

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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BeyondGender: A Multifaceted Bilingual dataset for Practical Sexism Detection 39

BeyondGender: A Multifaceted Bilingual Dataset for Practical...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xuan Yang, Li Zhang, Han Tu, Geng Wang, Qianlong Ding, keyang Fan, Chuang Li, Jing Xu, Ruifeng Harbin Institute of Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Research Centre on Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)157735897X

Sexism affects both women and men, yet research often overlooks misandry and suffers from overly broad annotations that limit AI applications. To address this, we introduce BeyondGender, a dataset meticulously annotated according to the latest definitions of misogyny and misandry. It features innovative multifaceted labels encompassing aspects of sexism, gender, phrasing, misogyny, and misandry. The dataset includes 6.0K English and 1.7K Chinese sexism instances, alongside 13.4K non-sexism examples. Our evaluations of masked language models and large language models reveal that they detect misogyny in English and misandry in Chinese more effectively, with F1-scores of 0.87 and 0.62, respectively. However, they frequently misclassify hostile and mild comments, underscoring the complexity of sexism detection. Parallel corpus experiments suggest promising data augmentation strategies to enhance AI systems for nuanced sexism detection, and our dataset can be leveraged to improve value alignment in large language models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A compound fault diagnosis method based on Multi-task Learning with Multi-gate Mixture-of-Experts 14

A compound fault diagnosis method based on Multi-task Learni...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Chen, Yanping Ren, Lele Xia, Hong Wang, Zhongmin Gao, Cong Wang, Fengwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-techRoadSouth Shenzhen518057 China

ISBN: (纸本)9781665499781

At present, the research object of fault diagnosis focuses on single fault diagnosis. This method of diagnosis does not consider the relationship between mechanical equipment faults, and cannot fully identify the type of fault. In the actual industrial process, mechanical equipment is mostly complex system, and compound fault diagnosis is more in line with industrial needs. In order to solve the problem of compound fault diagnosis of mechanical equipment and improve the accuracy of fault identification, this paper establishes a compound fault diagnosis model for gearbox by means of multi-task learning, uses multi-gated networks to extract features from the task sharing layer, and finally classifies the fault through the task specific *** experimental results show that, the compound fault diagnosis method based on Multi-task Learning with Multi-gate Mixture-of-Experts (ML-MMoE) can accurately identify compound fault types under different working conditions. © 2022 IEEE

关键词： Failure analysis

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