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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Enhancing Multimodal Tweet Analysis Accuracy through Integration of CLIP Model and Multi-layer Attention Mechanism 24

Enhancing Multimodal Tweet Analysis Accuracy through Integra...

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Proceedings of the 2024 8th International Conference on Natural Language Processing and Information Retrieval

作者： Yupu Liu Kazuyuki Matsumoto Trustworthy Multimodal Affective Intelligence and Knowledge Engineering Lab Tokushima University Tokushima Tokushima Japan

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Nickel-based metallurgical coating architectures for superior wear resistance in high-temperature P91 steel applications

Journal of Alloys and Metallurgical Systems

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Journal of Alloys and Metallurgical Systems 2025年 9卷

作者： Bhoskar, Avishkar Naik, Hardik Kalyankar, Vivek Deshmukh, Dhiraj Advanced Welding Lab. Department of Mechanical Engineering Sardar Vallabhbhai National Institute of Technology Surat395007 India Asian Lab Grown Diamonds Surat395004 India MET's Institute of Engineering Bhujbal Knowledge City Maharashtra Nashik India

This study delves into an improved hardfacing approach for addressing high-temperature wear issues in engineering valves used in power plants. The suggested approach uses plasma transfer arc welding (PTAW) of Colmonoy 6 with an SS-309L buffer layer on P91 steel (CoSP91). Comparative investigation shows that CoSP91 has a 67 % improvement in wear resistance at elevated temperatures (600°C), with a documented wear loss of 0.01239 g compared to present industry practices (StP91: 0.08593 g). This increase is due to the formation of Cr7C3, Cr2B, and Cr5B3 hard phases, as well as a protective oxide layer, that functions together to improve wear resistance. The findings demonstrate CoSP91 as a technically viable and cost-effective hardfacing solution for high-temperature components, with improved wear resistance and low cracking susceptibility. © 2025 The Authors

关键词： Wear resistance

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Randomized block Krylov method for approximation of truncated tensor SVD

arXiv

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arXiv 2025年

作者： Kooshkghazi, Malihe Nobakht Ahmadi-Asl, Salman de Almeida, André L.F. Lab of Machine Learning and Knowledge Representation Innopolis University Innopolis420500 Russia Department of Teleinformatics Engineering Federal University of Ceara Fortaleza Brazil

This paper is devoted to studying the application of the block Krylov subspace method for approximation of the truncated tensor SVD (T-SVD). The theoretical results of the proposed randomized approach are presented. Several experimental experiments using synthetics and real-world data are conducted to verify the efficiency and feasibility of the proposed randomized approach, and the numerical results show that the proposed method provides promising results. Applications of the proposed approach to data completion and data compression are presented. © 2025, CC BY.

关键词： Numerical methods

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LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

arXiv

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arXiv 2025年

作者： Hu, Yuxuan Zhang, Jing Chen, Xiaodong Zhao, Zhe Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China Engineering Research Center of Database and Business Intelligence Beijing China Tencent AI Lab Beijing China

Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. Despite these successes, such approaches suffer from an increased memory and computation overhead, which affects efficiency of LoRA methods. In response to this limitation, we introduce LoRS, an innovative method designed to achieve both memory and computation efficiency when fine-tuning sparse LLMs. To mitigate the substantial memory and computation demands associated with preserving sparsity, our approach incorporates strategies of weight recompute and computational graph rearrangement. In addition, we also improve the effectiveness of LoRS through better adapter initialization. These innovations lead to a notable reduction in memory and computation consumption during the fine-tuning phase, all while achieving performance levels that outperform existing LoRA approaches. © 2025, CC BY.

关键词： Problem oriented languages

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Glioblastoma tumor segmentation using an ensemble of vision transformers

Glioblastoma tumor segmentation using an ensemble of vision ...

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Medical Imaging 2025: Computer-Aided Diagnosis

作者： Liu, Huafeng Dowdell, Benjamin Engelder, Todd Pulmano, Zarah Osa, Nicolas Barman, Arko Department of Computer Science Rice University Houston United States Data to Knowledge Lab Rice University Houston United States Department of Electrical and Computer Engineering Rice University Houston United States Department of Statistics Rice University Houston United States

ISBN: (纸本)9781510685925

Glioblastoma is one of the most aggressive and deadliest types of brain cancer, with low survival rates compared to other types of cancer. Analysis of Magnetic Resonance Imaging (MRI) scans is one of the most effective methods for the diagnosis and treatment of brain cancers such as glioblastoma. Accurate tumor segmentation in MRI images is often required for treatment planning and risk assessment of treatment methods. Here, we propose a novel pipeline, Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET), which leverages MaskFormer, a vision transformer model, and generates robust tumor segmentation masks. We use an ensemble of nine predicted segmentation masks generated from three different models separately trained on each of the three orthogonal 2D slice directions (axial, sagittal, and coronal) of a 3D brain MRI volume. Each of the models in the ensemble consists of three modules - a pixel-level module that extracts per-pixel embeddings, a transformer module that generates per-segment embeddings, and a segmentation module that combines these embeddings to generate the segmentation mask. Two different data augmentation strategies were also implemented to improve the performance of the individual models. We trained, tuned hyperparameters, and tested our models on the publicly available UPenn-GBM dataset, consisting of 3D multi-parametric MRI (mpMRI) scans from 611 subjects. We use Dice coefficient (DC) and 95% Hausdorff distance (HD) for evaluating the performance of our models. Our models achieved state-of-the-art results in segmenting all three different tumor regions – tumor core (DC = 0.894, HD = 2.308), whole tumor (DC = 0.891, HD = 3.552), and enhancing tumor (DC = 0.812, HD = 1.608). © 2025 SPIE.

关键词： Magnetoencephalography

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CovKG: A Covid-19 knowledge Graph for enabling multidimensional analytics on Covid-19 epidemiological data considering spatiotemporal, environmental, health, and socioeconomic aspects

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International Journal of Information Management Data Insights 2025年第1期5卷

作者： Deb Nath, Rudra Pratap Raihan, S.M. Shafkat Das, Tonmoy Chandro Pedersen, Torben Bach Ghose, Debasish Big Data Information and Knowledge Engineering Lab Department of Computer Science and Engineering University of Chittagong Chattogram 4331 Bangladesh Department of Computer Science Aalborg University Aalborg 9220 Denmark School of Economics Innovation and Technology Kristiania University College State Three Bergen 0153 Norway

The Covid-19 pandemic is influenced by many environmental, health, and socioeconomic aspects such as air pollution, comorbidity, occupation, etc. To better manage future pandemics, decision-makers need comprehensive data on Covid-19 mortality and morbidity. Most Covid-19 data sources focus on spatiotemporal aspects, and existing research often overlook the combined impact of multiple interconnected factors. This study introduces a Covid-19 knowledge Graph (CovKG) derived from 20 data sources, enabling multidimensional analysis of epidemiological data, including time, location, temperature, comorbidity, occupation, and others. CovKG is modeled using RDF, connected to 10,951 external resources, and semantically enriched with Data Cube (QB) and QB for OLAP (QB4OLAP) vocabularies to adhere to the FAIR principles and ensure OLAP compatibility. Finally, we perform a qualitative and comparative evaluation and extract statistical insights across multiple dimensions of Covid-19 epidemiology. When assessed, CovKG answers 100% of competency queries, outperforming other data stores that only answer 39%. CovKG and its analytical interface are available at https://***/datasets/CovKG/. © 2025 The Authors

关键词： Covid-19 FAIR principles knowledge Graph Linked data Multidimensional analysis Semantic technology

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A Many-Objective Evolutionary Algorithm with Spatial Division and Angle Culling Strategy

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Chinese Journal of Electronics 2021年第3期30卷 437-443页

作者： WANG Hongbo YANG Fan TIAN Kena TU Xuyan School of Computer and Communication Engineering Beijing Key Lab.of Knowledge Engineering for Materials ScienceUniversity of Science and Technology Beijing

In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a Many-objective evolutionary algorithm based on a spatial division and angle-culling strategy(Ma OEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed Ma OEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of Ma OEA-SDAC in solving high-dimensional optimization problems has been verified.

关键词： reasonable balance many-objective evolutionary algorithm convergence environment selection stage angle culling strategy elimination strategy spatial division rich diversity Pareto optimisation angle-culling strategy diversity imbalance restricted matching selection specific project evolutionary computation fast convergence reorganization stage MaOEA-SDAC space division optimal solutions

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IKM_lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation 22

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning...

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22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023

作者： Wu, Yu-Hsuan Lin, Ying-Jia Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781959429852

This paper describes the entry by the Intelligent knowledge Management (IKM) laboratory in the BioLaySumm 2023 task1. We aim to transform lengthy biomedical articles into concise, reader-friendly summaries that can be easily comprehended by the general public. We utilized a long-text abstractive summarization longformer model and experimented with several prompt methods for this task. Our entry placed 10th overall, but we were particularly proud to achieve a 3rd place score in the readability evaluation metric. Our code is available at https://***/IKMlab/ BioLaySumm. © 2023 Association for Computational Linguistics.

关键词： knowledge management

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Database Research： Achievements and Challenges

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Journal of Computer Science & Technology 2006年第5期21卷 823-837页

作者：王珊杜小勇孟小峰陈红 School of Information Renmin University of China MOE Key Lab of Data Engineering and Knowledge Engineering Beijing 100872 P.R. China

Database system is the infrastructure of the modern information system. The R＆D in the database system and its technologies is one of the important research topics in the field. The database R＆D in China took off later but it moves along by giant steps. This report presents the achievements Renmin University of China （RUC） has made in the past 25 years and at the same time addresses some of the research projects we, RUC, are currently working on. The National Natural Science Foundation of China supports and initiates most of our research projects and these successfully conducted projects have produced fruitful results.

关键词： database management system parallel database systems Chinese database systems self-managing databases P2P computing and databases data warehousing OLAP Web data management XML mobile data management semantic web contextual information retrieval

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