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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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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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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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Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1134-1153页

作者： Jiahai Wang Yuyan Sun Zizhen Zhang Shangce Gao the Department of Computer Science and also with the Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of Educationand also with the Guangdong Key Laboratory of Big Data Analysis and ProcessingSun Yat-sen UniversityGuangzhou 510275China the Department of Computer Science Sun Yat-sen UniversityGuangzhou 510275China the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

关键词： Adaptive neighborhood selection manpower planning multiobjective optimization multitrip pickup and delivery problem with time windows

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The HuaBiao project:whole-exome sequencing of 5000 Han Chinese individuals

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Journal of Genetics and Genomics 2021年第11期48卷 1032-1035页

作者： Meng Hao Weilin Pu Yi Li Shaoqing Wen Chang Sun Yanyun Ma Hongxiang Zheng Xingdong Chen Jingze Tan Guoqing Zhang Menghan Zhang Shuhua Xu Yi Wang Hui Li Jiucun Wang Li Jin State Key Laboratory of Genetic Engineering Collaborative Innovation Center for Genetics and DevelopmentHuman Phenome InstituteFudan UniversityShanghai 200433China State Key Laboratory of Genetic Engineering Collaborative Innovation Center for Genetics and DevelopmentHuman Phenome InstituteInstitute for Six-sector EconomyFudan UniversityShanghai 200433China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesInstitute of Archaeological ScienceFudan UniversityShanghai 200433China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesFudan UniversityShanghai 200433China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesInstitute for Six-sector EconomyFudan UniversityShanghai 200433China Taizhou Institute of Health and Sciences Fudan UniversityTaizhouJiangsu 225300China Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesHuman Phenome InstituteFudan UniversityShanghai 200433China

Next-generation sequencing technologies have significantly accelerated the identification of disease-causing mutations and facilitated the emergence of personalized medicine(Genomes Project Consortium et al.,2015;Goodwin et al.,2016;Sirugo et al.,2019).In comparison with whole-genome sequencing,whole-exome sequencing(WES),which covers the coding regions of the genome,offers a cost-efficacy *** provides deeper sequencing depth(>100)and allows the more accurate detection of rare variants that are tailored for clinical applications(Lek et al.,2016).

关键词： exo cost rare

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature 23

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Zou, Huiying Duan, Jia Liu, Xi Ren, Wei Li, Tao Zheng, Xianghan Choo, Kim-Kwang Raymond Hunan Engineering Research Center of Geographic Information Security and Application The Third Surveying and Mapping Institute of Hunan Province Hunan China China University of Geosciences School of Computer Science Wuhan China Sichuan University Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Casm State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Mnr Beijing China Fuzhou University College of Computer and Big Data China Sanya College School of Information and Intelligent Engineering China University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio United States

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach's security. © 2024 IEEE.

关键词： blockchain transaction cross-chain technology notary schemes proxy re-signature

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

Logical Relation Modeling and Mining in Hyperbolic Space for...

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International Conference on data engineering

作者： Yanchao Tan Hang Lv Zihao Zhou Wenzhong Guo Bo Xiong Weiming Liu Chaochao Chen Shiping Wang Carl Yang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Department of Computer Science Emory University Atlanta United States

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity- based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable.

关键词： Measurement Accuracy Taxonomy Semantics Predictive models Performance gain Linguistics

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A new steganography without embedding based on adversarial training 20

A new steganography without embedding based on adversarial t...

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2020 ACM Turing Celebration Conference - China, ACM TURC 2020

作者： Jiang, Wenjie Hu, Donghui Yu, Cong Li, Meng Zhao, Zhong-Qiu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education and College of Computer Science and Information Engineering !!!Hefei University of Technology Hefei China College of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781450375344

Steganography is an art to hide information in the carriers to prevent from being detected, while steganalysis is the opposite art to detect the presence of the hidden information. With the development of deep learning, several state-of-the-art steganography and steganalysis based on deep learning techniques have been proposed to improve hiding or detection capabilities. Generative Adversarial Networks (GANs) based steganography directly uses the minimax game between the generator and discriminator, to automatically generate steganography algorithms resisting being detected by powerful steganalysis. The steganography without embedding (SWE) based on GANs, where the generated cover images themselves are stego ones carrying secret information has shown its state-of-the-art steganography performance. However, SWE based on GANs has serious weaknesses, such as low information recovery accuracy, low steganography capacity and poor natural showing. To solve these problems, this paper proposes a new SWE based on adversarial training, with carefully designed generator, discriminator and extractor, as well as their loss functions and optimized training mode. The proposed method can achieve a very high information recovery accuracy (100% in some cases), and at the same time improve the steganography capacity and image quality. © 2020 ACM.

关键词： Steganography

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On the Stability and Generalization of Triplet Learning

arXiv

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arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Encoding Global Semantic and Localized Geographic Spatial-Temporal Relations for Traffic Accident Risk Prediction

SSRN

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SSRN 2024年

作者： Alhaek, Fares Li, Tianrui Rajeh, Taha M. Javed, Muhammad Hafeez Liang, Weichao School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

The proliferation of vehicles and the intricate layout of road systems have contributed to a significant rise in traffic accidents, posing a pressing concern globally. Despite the advancements facilitated by deep learning, several challenges persist in the domain of traffic accident prediction. Firstly, the sparsity of accident data in certain city regions presents a significant obstacle, particularly when attempting fine-grained predictions at a local level. Secondly, the intricate spatial-temporal relations inherent in traffic accident data pose a challenge for existing prediction models. In response to these formidable challenges, this paper presents GLST-TARP, a novel model for predicting traffic accident risk in urban environments by leveraging both global semantic and localized geographic spatial-temporal relations. Specifically, we construct multi-graphs to encode global static and dynamic spatial relations, incorporating attention mechanisms to adaptively focus on relevant information and temporal relations. Additionally, we employ channel-wise convolutional neural network blocks to extract localized geographic features and enhance predictive accuracy. The proposed model is trained using a Huber loss function tailored for regression tasks, relieving the impact of zero values during optimization. Experimental results demonstrate that GLST-TARP outperforms state-of-the-art methods in predicting traffic accident risks, showcasing its potential for enhancing urban safety and transportation management. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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