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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Explore Statistical Properties of Undirected Unweighted Networks from Ensemble Models 27th

Explore Statistical Properties of Undirected Unweighted Netw...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Xunda Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9783031783975

Complex network theory has been widely demonstrated as a powerful tool in modeling and characterizing various complex systems. In the past, complex network theory has focused on the behaviors as well as the characteristics of the network nodes and edges. However, with the continuous evolution of society, traditional graph theory faces challenges due to the emergence of extermely large network structures. Recently, complex network method based statistics has attracted much attention. The new approach effectively manages very large networks and uncovers their intrinsic properties. In this paper, we present a complex network analysis model for undirected, unweighted networks based on a statistical analysis approach. This model is inspired by the ensemble model in thermostatistical physics. Based on the established mathematical model, we derive physical measures that reflect the intrinsic properties of the network, including Entropy, Free Energy, Temperature, and so on. In the experimental part, we first explored the mathematical characterization of these metrics. Then, we observed the performance of various network categories under the same metric. Finally, we applied these measures to the field of graph classification. Extensive experiments demonstrate the effectiveness and superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Undirected graphs

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks 9th

Leveraging user-item interactions and hotel characteristics ...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Panarto, Francis Alexander Phanghegar, Tyrone Matthew Gunardi, Vincent Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Travelers usually check information about the destination before they decide to go. However, sometimes the information is too much to handle and causes confusion. A filtering mechanism is needed to help them make their choices. The solution is to implement a recommendation system;some common techniques that are used for this are Collaborative Filtering and content-based filtering, but both techniques have their flaws. Therefore, we propose a solution to combine those two methods through Graph Neural Network architectures to help fix their flaws. The proposed method will be used for recommending hotels in Indonesia using the HotelRec dataset as the base dataset and hotel features acquired from the TripAdvisor API. We use several GNN architectures, such as GCN, LightGCN, GraphSAGE, and GAT, to implement our approach. Then, we compare it to several baseline models, such as SVD and NeuMF, which are common baselines in recommendation systems. The performance of these models is calculated based on three metrics: HitRate@K, NDCG@K, and Recall@K. Based on the result of this experiment, we found that incorporating hotel features into the learning process of the model, accompanied by user-item interactions, boosted the model's performance by at least 20% in all metrics, especially in datasets with a smaller number of interactions. © 2024 The Authors.

关键词： Collaborative filtering

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Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows 24

Enhancing Progressive Diagnosis Prediction in Healthcare wit...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Tan, Yanchao Zhang, Hengyu Zhou, Zihao Ma, Guofang Wang, Fan Liu, Weiming Liao, Xinting Hertzberg, Vicki S. Yang, Carl College of Computer and Data Science Fuzhou University China College of Computer Science Zhejiang University China School of Computer Science Zhejiang Gongshang University China Nell Hodgson Woodruff School of Nursing Emory University United States Department of Computer Science Emory University United States

ISBN: (纸本)9798400701726

Progressive diagnosis prediction in healthcare is a promising yet challenging task. Existing studies usually assume a pre-defined prior for generating patient distributions (e.g., Gaussian). However, the inferred approximate posterior can deviate from the real-world distribution, which further affects the modeling of continuous disease progression over time. To alleviate such inference bias, we propose an enhanced progressive diagnostic prediction model (i.e., ProCNF), which integrates continuous normalizing flows (CNF) and neural ordinary differential equations (ODEs) to achieve more accurate approximations of patient health trajectories while capturing the continuity underlying disease progression. We first learn patient embeddings with CNF to construct a complex posterior approximation of patient distributions. Then, we devise a CNF-enhanced neural ODE module for progressive diagnostic prediction, which aims to improve the modeling of disease progression for individual patients. Extensive experiments on two real-world longitudinal EHR datasets show significant performance gains brought by our method over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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Rating Text Classification with Weighted Negative Supervision on Classifier Layer

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Chinese Journal of Electronics 2023年第6期32卷 1304-1318页

作者： ZHANG Jun QIU Longlong SHEN Fanfan HE Yueshun TAN Hai HE Yanxiang Radiological Geosciences Big Data Engineering Laboratory of Jiangxi Province East China University of Technology School of Information Engineering East China University of Technology School of Computer Science Nanjing Audit University Computer School Wuhan University

Bidirectional encoder representations from transformers(BERT) gives full play to the advantages of the attention mechanism, improves the performance of sentence representation, and provides a better choice for various natural language understanding(NLU)tasks. Many methods using BERT as the pre-trained model achieve state-of-the-art performance in almost various text classification scenarios. Among them, the multitask learning framework combining the negative supervision and the pre-trained model solves the issue of the model performance degradation that occurs as the semantic similarity of texts conflicts with the classification standards. The current model does not consider the degree of difference between labels, which leads to insufficient difference information learned by the model, and affects classification performance, especially in the rating classification tasks. On the basis of the multi-task learning model, this paper fully considers the degree of difference between labels, which is expressed by using weights to solve the above problems. We supervise negative samples on the classifier layer instead of the encoder layer, so that the classifier layer can also learn the difference information between the labels. Experimental results show that our model can not only performs well in 2-class and multi-class rating text classification tasks, but also performs well in different languages.

关键词： Training Text categorization Semantics Bidirectional control Transformers Multitasking Encoding

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Quantifying Racial Segregation Through Continuous-Time Quantum Walks 27th

Quantifying Racial Segregation Through Continuous-Time Quant...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Yutong Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China Xi’an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9783031781919

With the development of cities, racial segregation is one of the reasons for social inequality on a large scale, it is also a major factor affecting economic development. However, racial segregation has received comparatively limited attention in research. In the paper, we propose a segregation index independent of any parameters( titled Quantum Walk Convergence Time), which calculates the convergence time statistics for accessing different racial categories through quantum walks on complex networks constructed from urban systems. The magnitude of the time statistics represents the degree of racial segregation. The results generated by our method can also be applied to other social factors, including family situation, income level, and education level. We evaluate our method using large-scale real datasets, including statistics from the U.S. Census Bureau and the Office for National Statistics in the UK. The results demonstrate the close correlation between our method and the degree of racial segregation, showing advantages over traditional random walk methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Continuous time systems

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A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Class Breast Cancer Classification 2

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Cla...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kansal, Kanika Kansal, Kajal Department of Computer Science and Engineering Data Science ABES Engineering College Uttar Pradesh Ghaziabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530389

Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survival rates. An automatic diagnosis system based on deep learning models is critical for improving detection, prediction accuracy, and survivability. This study used various DenseNet-based architectures for multi-class classification using the BUSI dataset. The experiments indicate that the fine-tuned DenseNet201 model outperforms with an accuracy of 98.28%. The results demonstrate the promising capability of the fine-tuned DenseNet201 model to have a high potential for further implementation in practice for achieving early diagnosis and highly individualized approaches for better treatment. The further development of extensive and diverse data sets will be essential for developing new diagnostic methods and improving the existing ones. © 2025 IEEE.

关键词： Lung cancer

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SARA: A Sparsity-Aware Efficient Oblivious Aggregation Service for Federated Matrix Factorization 25th

SARA: A Sparsity-Aware Efficient Oblivious Aggregation Servi...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Zheng, Yifeng Xiong, Tianchen Ouyang, Huajie Wang, Songlei Hua, Zhongyun Gao, Yansong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Data61 CSIRO Sydney Australia

ISBN: (纸本)9789819605668

Federated matrix factorization (FedMF) has recently emerged as a privacy-friendly paradigm which runs matrix factorization (MF) in a federated learning (FL) setting and enables users to keep their individual rating data local in the training process. In FedMF, users only need to share out item gradients for aggregation. However, prior work has shown that the item gradients, if directly exposed, can still leak users’ rating data. Meanwhile, the rating data are typically sparse and simply uploading the gradients of rated items will leak which and how many items a user rates. In light of the above, in this paper, we present SARA, a new sparsity-aware efficient oblivious aggregation service for FedMF. SARA protects the confidentiality of item gradients as well as hides which and how many items a user rates, through a custom sparsity-aware design that delicately builds on differential privacy and lightweight cryptography. Extensive experiments over real-world datasets demonstrate that SARA is utility-preserving and can bring the users significant savings (up to 98.28%) in the number of transmitted item gradients, compared to the baseline of fully transmitting gradients for all possible items. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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