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31st International Conference on Computational Linguistics, COLING 2025

作者： Tran, Khai Phan Hua, Wen Li, Xue School of Electrical Engineering and Computer Science The University of Queensland Australia Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761964

Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE's latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE. Our code is released at: https://***/khaitran22/VaeDiff-DocRE. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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An Integrated Learning Model for Land Use Classification and Prediction

An Integrated Learning Model for Land Use Classification and...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Supreeth, A. Jeeva Ganesh, A. Subash, N. Santhosh, R. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Accurate and efficient land-use classification is important for the formulation of plans by urban planners, environmental monitoring, and even sustainable development. The paper explores the use of transfer learning methods that utilize pre-trained models to improve LSTM and DNNs' performance when applied on metropolitan settings for LAC. This can be done using transfer learning to learn from large datasets and adapt to the complexities of urban land-use patterns even with limited labeled data. This study uses the UC-Merced-LandUse dataset and heatmap analysis to visualize the models' classification decisions in demonstrating the effectiveness of this approach. This research contributes to the advancement of land-use classification techniques, which enable more accurate and informed urban planning and environmental management. © 2025 IEEE.

关键词： Prediction models

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A Preventive Environment for Pest Over the Crops Using Simplified Network Architecture 4

A Preventive Environment for Pest Over the Crops Using Simpl...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kuhanraja, A.R. Ramkumar, P. Balaji, S. Santhosh, R. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Crop pests can significantly impact productivity, making early detection and prevention crucial for management. Machine learning approaches are being used to forecast crop diseases using a variety of data sets. Relatively easy access to the environment data has resulted in a lot of attempts at predicting diseases and pests using this data. In this research, we provide a model that uses deep learning technique known as Simplified YOLO 3.1 architecture to examine past crop growth environment data to forecast illnesses. Utilizing large public datasets on crops such as tomato, pepper, grape, strawberry, and paprika, we show that the model is capable of accurately forecasting crop pest and disease risk. With an average accuracy of 98% and precision of 96%, it showed excellent predictive power. The anticipated results can help with post-harvest processing or insect control. It is expected that this learning framework and crop disease prediction model, which is based on environmental data, will be extensively useful for the prevention of pests and diseases in various facilities and crops. © 2025 IEEE.

关键词： Plant diseases

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Automation of IoT Network Traffic Analysis and Anomaly Detection using Deep Packet Inspection with Hybrid DenseNet121-BiLSTM 1

Automation of IoT Network Traffic Analysis and Anomaly Detec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Data Science Coimbatore India Department of Mathematics Thrissur India Department of Mathematics Mala India

ISBN: (纸本)9798331529246

Network security is one of the most crucial problems facing in Internet of Things. An essential component of information systems' long-term viability and regular functioning is network intrusion detection. One potential technique that aids in the identification and defense against network threats is deep packet inspection (DPI). A novel DPI method utilizing a blend of LSTM, GRU, and Recurrent Neural Networks is presented in the paper. The proposed model enhances the accuracy and reliability of detecting outcomes for the threats included in network traffic data. The proposed system detects and identify particular threats, such as malware exploitation, DoS attacks, and unauthorized access attempts, allowing linked devices and services with an accuracy of 99.99%. The system, therefore, can be applied in real-time monitoring systems, intrusion detection and prevention systems, and forensic investigations. Through a series of quantitative trials and comparisons with the most recent formulations, the effectiveness of this approach is assessed. © 2025 IEEE.

关键词： Network intrusion

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data-Efficient Radiology Report Generation via Similar Report Features Enhancement 3rd

Data-Efficient Radiology Report Generation via Similar Repo...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

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Journal of Umm Al-Qura University for Engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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Optical Character Recognition using YOLO4, Tesseract and Paddleocr for Text Extraction 3

Optical Character Recognition using YOLO4, Tesseract and Pad...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kusuma, T. Jyothi, S. Shruthi, G. Sheela, S. Naveen Kumar, C. Ramaiah Institute of Technology Department of Artificial Intelligence & Data Science Bangalore India Global Academy of Technology Department of Computer Science & Engg Bangalore India Ramaiah Institute of Technology Department of Information Science Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The recent advancement Tesseract OCR engine and the YOLO4 (You Only Look Once version 4) object detection framework provide an innovative approach to optical character recognition (OCR) with a focus on table extraction. Because of their complicated layouts, traditional OCR algorithms frequently have difficulty accurately retrieving tabular data from photos. The suggested technique successfully extracts tables from photos by utilizing Tesseract for precise character recognition and YOLO4 for reliable object detection. Table borders, cell boundaries, and text sections inside tables are all recognized by the YOLO4 model after it has been trained. The textual information inside these areas is then identified using Tesseract. The suggested solution outperforms conventional OCR techniques in experimental results that show how well it extracts tables from photos with different layouts and levels of complexity. © 2025 IEEE.

关键词： data Analytics

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on Engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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WavFace: A Multimodal Transformer-Based Model for Depression Screening

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3632-3641页

作者： Flores, Ricardo Tlachac, M.L. Shrestha, Avantika Rundensteiner, Elke A. Universidad de Concepción Department of Computer Science Concepción Chile Worcester Polytechnic Institute Department of Data Science WorcesterMA01609 United States Bryant University Department of Information Systems and Analytics Center for Health and Behavioral Sciences SmithfieldRI01609 United States

Depression, a prevalent mental health disorder with severe health and economic consequences, can be costly and difficult to detect. To alleviate this burden, recent research has been exploring the depression screening capabilities of deep learning (DL) models trained on videos of clinical interviews conducted by a virtual agent. Such DL models need to consider the challenges of modality representation, alignment, and fusion as well as small sample sizes. To address them, we propose WavFace, a multimodal deep learning model that inputs audio and temporal facial features. WavFace adds an encoder-transformer layer over pre-trained models to improve the unimodal representation. It also applies an explicit alignment method for both modalities and then uses sequential and spatial self-attention over the alignment. Finally, WavFace fuses the sequential and spatial self-attentions among the two modality embeddings, inspired by how mental health professionals simultaneously observe visual and vocal presentation during clinical interviews. By leveraging sequential and spatial self-attention, WavFace outperforms pre-trained unimodal and multimodal models from the literature. With a single interview question, WaveFace screened for depression with a balanced accuracy of 0.81. This presents a valuable modeling approach for audio-visual mental health screening. © 2013 IEEE.

关键词： Electronic health record

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