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MCAF: Multi-Headed Causal Attention Fusion Approach for Multimodal Sentiment Analysis

MCAF: Multi-Headed Causal Attention Fusion Approach for Mult...

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International Joint Conference on Neural networks (IJCNN)

作者： Qingao Wang Xingang Wang Jinxiao Yang Yurun Bi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multimodal sentiment analysis(MSA) aims to synthesize multiple pieces of information, including textual, visual, and auditory information, to more accurately infer sentiment polarity. Due to the existence of complex and diverse affective causal relationships between different modalities, they interact with each other and constitute a multidimensional expression of human emotions. In the face of unbalanced data, i.e., when there are significant differences in the number of samples between different categories, the attention mechanism of the model tends to focus on spurious correlations in the training data, seriously impairing the model’s generalization ability and leading to bias, unbalanced prediction, and false correlation identification problems. To address the above problems, this study proposes a new multimodal fusion method based on the "within-sample sampling" and "cross-sample sampling" front door adjustment methods in causal inference strategies: the multi-head causal attention fusion method (MCAF). This method aims to capture potential causal relationships between different modalities through different attention heads, helping the model focus more on key causal features and avoid paying too much attention to false correlations in the training data. This helps improve the generalization performance of the model, make the decision-making process of the model more interpretable, and enhance the accuracy and robustness of the model for multimodal sentiment analysis tasks, making it more suitable for practical application scenarios.

关键词： Analytical models Sentiment analysis Visualization Correlation Neural networks Training data Predictive models

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A Money Laundering Structure Detection Method Based on Incremental Transaction Analysis

A Money Laundering Structure Detection Method Based on Incre...

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Data Science and information Technology (DSIT), International Conference on

作者： Yifei Meng Zhong Li Key Laboratory of Embedded System and Service Computing Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China College of Information Science and Technology Donghua University Shanghai China

Money laundering is the process of legitimizing dirty money through complex transactions, posing a serious threat to a country’s financial stability and national security. Nowadays, with the prevalence of organized money laundering, launderers prefer to use intricate multi-hop laundering chains to transfer dirty money. Moreover, they engage in normal financial activities to disrupt detection by auditors. In response to these trends and challenges, we propose a novel unsupervised money laundering structure detection framework for the anti-money laundering field, called structure incremental expansion (SIE). Our framework consists of two main modules: 1) Initialization of suspicious structures, which adopts a control-limit based method to identify suspicious accounts exhibiting anomalous transaction behavior. These accounts will serve as the starting points for suspicious structure expansion. 2) Dynamic structure expansion, where we design three dynamic membership functions according to the Financial Action Task Force’s definitions of the three stages of money laundering evolution. Newly-added incremental transactions in the network will be assigned to appropriate expanding suspicious structures. We conduct extensive experiments on simulated and public financial networks. SIE exhibits desirable performance and scalability. We also provide a detailed case study, visualizing a complete money laundering structure detection process, demonstrating our method’s strong interpretability.

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Research on Electric power Emergency Plan Instruction Generation Method Based on Natural Language Processing

Research on Electric Power Emergency Plan Instruction Genera...

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2nd International Conference on Artificial Intelligence and Communication Technologies, ICAICT 2024

作者： Li, Tao Zhang, Zehao Feng, Jie Liu, Yitong Zhao, Jinxiong Ma, Zhicheng State Grid Corporation of China Beijing100031 China State Grid Smart Grid Research Institute Co. LTD Beijing102206 China State Grid Key Laboratory of Information and Network Security Nanjing China State Grid Gansu Electric Power Company Electric Power Scientific Research Institute Lanzhou730070 China

ISBN: (纸本)9789819600915

At present, the State Grid Corporation has established a relatively perfect emergency plan system, but the emergency plan still stays on the "paper," which is not conducive to the quick search for specific disposal measures, and the command personnel are more based on the past experience in the face of complex and changeable emergency scenarios. In order to realize real-time and instruction-oriented interaction between electric power emergency plan and emergency personnel, it is urgent to study the generation method of electric power emergency plan information interactive instruction. In the process of generating electric power emergency plan instruction, it is necessary to build the relationship between instruction template and labels according to the actual emergency types and related emergency plans. It is necessary to divide the emergency plan into sections, clauses and words, and extract the entity corresponding to the specific event type, stage, link, scenario, executor, trigger condition, prerequisite, recipient, task measure, resource demand, and other labels in the text. This paper uses computer natural language processing technology to realize the generation of electric power emergency plan instructions, improve the digital degree of emergency plan, and lay a foundation for the wisdom of emergency plan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Opinion-Tree-guided Contrastive Learning for Aspect Sentiment Quadruple Prediction

Opinion-Tree-guided Contrastive Learning for Aspect Sentimen...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhijun Li Zhenyu Yang Yiwen Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, aspect sentiment quadruple prediction (ASQP) has become a popular task in the field of aspect-based sentiment analysis. When modeling comment statements, it is often necessary to consider structural information between sentiment labels. Existing approaches categorize these labels by encoding text and dependency tree structures separately and then incorporating their representations, but this approach fails to effectively capture the structural information between sentiment labels. To address this problem, we propose a method for ASQP called Opinion-Tree-guided Contrastive Learning (OTCL), which embeds structural information directly into the text instead of modeling it separately. Specifically, OTCL utilizes the opinion tree structure to guide the construction of positive samples of the input text during training. By associating the input text with its positive samples, the text encoder can learn to generate textual representations associated with the opinion tree structure. We validate the effectiveness of our method on two commonly used datasets.

关键词： Training Sentiment analysis Federated learning Contrastive learning Predictive models Encoding Task analysis

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Lu Jing Zhao Lichao Ding Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method.

关键词： Training Uncertainty Soft sensors Noise Training data Knowledge graphs Data models Stability analysis Reliability Thermal stability

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HCF-Net: Hierarchical Context Fusion network for Infrared Small Object Detection

HCF-Net: Hierarchical Context Fusion Network for Infrared Sm...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shibiao Xu Shuchen Zheng Wenhao Xu Rongtao Xu Changwei Wang Jiguang Zhang Xiaoqiang Teng Ao Li Li Guo School of Artificial Intelligence Beijing University of Posts and Telecommunications CASIA MAIS School of Artificial Intelligence University of Chinese Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science School of Computer Science and Technology Harbin University of Science and Technology

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet.

关键词： Deep learning Location awareness Convolutional codes Computer vision Adaptation models Image recognition Object detection

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Research and Implementation of a Fast Fault Location Algorithm for Distribution network Equipment

Research and Implementation of a Fast Fault Location Algorit...

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International Conference on Applied Machine Learning (ICAML)

作者： Junfeng Qiao Aihua Zhou Lin Peng Xiaofeng Shen Sen Pan Jingde Chen State grid key laboratory of information & Network Security of State Grid Smart Grid Research Institute Co. LTD Nanjing Jiangsu China Qingpu Power Supply Company State Grid Shanghai Municipal Electric Power Company. Shanghai China

The new distribution network management system is mainly a system that integrates automation and intelligent data acquisition technology, computer technology and business management system, and manages all aspects of the electric distribution grid. The traditional fault analysis of electric distribution grid equipment is to judge the relative position relationship between the widely distributed switches and segmented switch-gear in the electric distribution grid, which can find out the real location of equipment fault through section analysis. This method has a low degree of automation. Later, through the design and development of intelligent computer programs, feeder terminal components are integrated into the conventionalizing switch in the network, through which the state data of the switch is collected, and then the collected data is transmitted to the central controller, which is identified and analyzed by the computer system. After receiving the collected data, after judgment and analysis, specific instructions are issued to operate the switch accordingly, Isolate the faulty equipment in a specific area to ensure that it does not affect other non faulty areas. In the article, a method of fault identification is proposed, which integrates the geographic location information into the computer program, and then performs signal dispatching through instruction analysis to realize the operation monitoring, data signal acquisition, equipment fault location, load control, etc. of the distribution network equipment.

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Continuous Verification of Catastrophic Recalling in Machine Unlearning via Adversarial Testing

Continuous Verification of Catastrophic Recalling in Machine...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Y. Neil Qu Zonghao Ji Lei Cui Chong Zhang Lei Liu Zhiyi Tian Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia Guangzhou Institute of Technology Xidian University Guangzhou China School of Computer Science University of Technology Sydney NSW Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms.

关键词： Privacy Machine learning algorithms Cyberspace Machine learning Data science Data models Testing

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News Dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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Overcoming Data Limitations and Cross-Modal Interaction Challenges in Medical Visual Question Answering

Overcoming Data Limitations and Cross-Modal Interaction Chal...

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International Joint Conference on Neural networks (IJCNN)

作者： Yurun Bi Xingang Wang Qingao Wang Jinxiao Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical Visual Question Answering (Med-VQA) is a sophisticated multimodal technology within the medical domain that utilizes natural language processing and artificial intelligence techniques. It serves as a tool in the medical field to provide information and answers, aiming to correctly respond to medical questions related to specific professional medical images. The primary goal is to assist patients in understanding their physical condition and provide decision support for healthcare professionals. While Generalized Visual Question Answering has seen significant advancements, challenges persist in the medical domain. Firstly, medical data has inherent limitations, such as the scarcity of annotated medical datasets and the complexity of medical images (including factors like noise, artifacts, and low contrast). Secondly, in terms of cross-modal fusion, there exists an intricate relationship between medical images and textual information. The complexity of the relationship between medical images and medical text, as well as the incorporation of global contextual details, pose challenges. To address these issues, this paper proposes the Feature-Enhanced network with Conditional Mixing. This innovative approach aims to overcome the scarcity and complexity of medical images without the need for additional datasets. Simultaneously, to tackle the challenge of cross-modal fusion and information interaction and to address the complex relationship between medical images and text, we propose the Cross-Adaptive Fusion network. Experimental results conducted on the VQA-RAD dataset demonstrate noticeable improvements in accuracy compared to mainstream Med-VQA models.

关键词： Visualization Accuracy Noise Neural networks Medical services Question answering (information retrieval) Data models

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