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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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Advanced functional connectivity analysis with integrated EEG signal enhancement and GTW-EEG-GAN for emotion detection

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International Journal of Computers and Applications 2024年第12期46卷 1152-1165页

作者： Babu, Nirmal Varghese Kanaga, E. Grace Mary Division of Computer Science and Engineering School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Data Science and Cyber Security School of Computer Science and Technology Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techniques by employing Graph Neural Networks (GNNs) on functional connectivity matrices derived from EEG data to advance emotion recognition. Our approach integrates sophisticated preprocessing methods, including Integrated EEG Signal Enhancement (IESE) and novel augmentation techniques such as Gaussian Time Warping Generative Adversarial Network (GTW-EEG-GAN). This integration aims at enhancing data quality and diversity. Signal decomposition using EMD-Wavelet Hybrid Decomposition further refines feature extraction, enabling robust analysis of EEG signals. Functional connectivity metrics capture critical neural interactions intended for precise emotion characterization. The GNN architecture effectively processes these features, achieving significant accuracy improvements. Evaluations on the DEAP dataset demonstrate promising results, attaining 94.5% accuracy before hyperparameter tuning and further improving to 98.6% after tuning, highlighting the system's efficacy. The methodology showcases the integration of advanced signal processing techniques with AI, offering a comprehensive framework for future studies. The findings advocate potential applications in areas such as personalized mental health monitoring, adaptive learning environments, and responsive gaming experiences, demonstrating the broad impact and versatility of AI-driven EEG-based emotion recognition. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Emotion Recognition

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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

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Mathematical Biosciences and engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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QRCODE: Quasi-Residual Convex Deep Network for Fusing Misaligned Hyperspectral and Multispectral Images

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-15页

作者： Lin, Chia-Hsiang Hsu, Chih-Chung Young, Si-Sheng Hsieh, Cheng-Ying Tai, Shen-Chieh National Cheng Kung University Department of Electrical Engineering Miin Wu School of Computing Tainan70101 Taiwan Institute of Data Science Miin Wu School of Computing National Cheng Kung University Tainan70101 Taiwan National Cheng Kung University Institute of Computer and Communication Engineering Department of Electrical Engineering Tainan70101 Taiwan Institute of Data Science National Cheng Kung University Tainan70101 Taiwan

Considering that hyperspectral image (HSI) is often of lower spatial resolution when compared to multispectral image (MSI), an economical approach for obtaining a high-spatial-resolution (HSR) HSI is to fuse the acquired HSI and MSI, thereby greatly facilitating the subsequent material identification and classification in satellite remote sensing. As satellite-acquired HSI and MSI are often misaligned, the proposed deep neural network does not require the input HSI/MSI to be spatially co-registered, making the challenging fusion network design even more difficult. In this study, we propose a streamlined and efficient convex model integrated into the subnetwork, which obviates the need for complex network structures in learning spatial-spectral relationships, effectively guiding the quasi-residual learning task in our alignment-free fusion network. The convex subnetwork is a low-rank model that leverages the convex geometric structure implicitly embedded in the hyperspectral signature space. To address the misalignment between HSI and MSI effectively, we introduce a novel shifted window attention module (SWAM) that exploits the neighboring correlation in the feature domain, significantly enhancing the performance and stability of the fusion task. Capitalizing on the redundancy among spectrums, we employ grouped convolution to decrease the computational complexity without causing additional performance degradation. The proposed quasi-residual convex deep (QRCODE) network demonstrates the state-of-the-art performance in alignment-free HSI/MSI fusion tasks. © 1980-2012 IEEE.

关键词： Deep neural networks

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QuMIN: quantum multi-modal data fusion for humor detection

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Multimedia Tools and Applications 2025年第18期84卷 18855-18872页

作者： Phukan, Arpan Haq Khan, Anas Anwarul Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801106 India Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India School of AI and Data Science Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

Humour detection has attracted considerable attention due to its significance in interpreting dialogues across text, visual, and acoustic modalities. However, effective methods to map correlations among different modalities remain an active area of research. In this study, we go beyond traditional machine learning techniques by introducing a Variational Quantum Circuit (VQC) that capitalizes on the inherent quantum properties of superposition, entanglement, and interference. Our proposed model, Quantum Multi-Modal data Fusion (QuMIN), is designed to better capture and reproduce the interaction across modalities, as well as the internal correlations within each modality. Our introduction of the novel VQC, which augments the DialogueRNN baseline with only an additional 4,809 parameters, signifies a substantial advancement in multi-modal humor detection with improvements of 12.34% in precision, 8.84% in recall and 10.57% in F1 score compared to the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images

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Multimedia Tools and Applications 2024年 1-21页

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © The Author(s) 2024.

关键词： Semantic Segmentation

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Benchmark experiment on slab^(238)U with D-T neutrons for validation of evaluated nuclear data

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Nuclear science and Techniques 2024年第2期35卷 145-159页

作者： Yan-Yan Ding Yang-Bo Nie Yue Zhang Zhi-Jie Hu Qi Zhao Huan-Yu Zhang Kuo-Zhi Xu Shi-Yu Zhang Xin-Yi Pan Chang-Lin Lan Jie Ren Xi-Chao Ruan Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Institute of Fluid Physics China Academy of Engineering PhysicsMianyang 62100China

A benchmark experiment on^(238)U slab samples was conducted using a deuterium-tritium neutron source at the China institute of Atomic *** leakage neutron spectra within energy levels of 0.8-16 MeV at 60°and 120°were measured using the time-of-flight *** samples were prepared as rectangular slabs with a 30 cm square base and thicknesses of 3,6,and 9 *** leakage neutron spectra were also calculated using the MCNP-4C program based on the latest evaluated files of^(238)U evaluated neutron data from CENDL-3.2,ENDF/B-Ⅷ.0,JENDL-5.0,and *** on the comparison,the deficiencies and improvements in^(238)U evaluated nuclear data were *** results showed the following.(1)The calculated results for CENDL-3.2 significantly overestimated the measurements in the energy interval of elastic scattering at 60°and 120°.(2)The calculated results of CENDL-3.2 overestimated the measurements in the energy interval of inelastic scattering at 120°.(3)The calculated results for CENDL-3.2 significantly overestimated the measurements in the 3-8.5 MeV energy interval at 60°and 120°.(4)The calculated results with JENDL-5.0 were generally consistent with the measurement results.

关键词： Leakage neutron spectra Uranium D-T neutron source Evaluated nuclear data

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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ViGT: proposal-free video grounding with a learnable token in the transformer

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science China(Information sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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