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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sanghyun Cho Minho Kim Hye-Lynn Kim Jung-Hun Lee Hyuk-Chul Kwon Department of Information Convergence Engineering Pusan National University Busan South Korea Division of Artificial Intelligence Engineering Korea Maritime and Ocean University Busan South Korea R&D Center AI LAB HDC LABS Seoul South Korea Department of Artificial Intelligence Dong-Eui University Busan South Korea Department of Computer Science and Engineering Pusan National University Busan South Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

Table question answering datasets have been released in various table sources and diverse tasks. However, most of these table question answering datasets are constructed in resource-rich languages like English. For low-resource languages, it is necessary to create new datasets or translate existing ones to train models on table question answering tasks. This process requires substantial costs and manpower. Table data, unlike natural text, is structured in a way that is less affected by word order and language-specific characteristics. In this paper, we propose a method to minimize the costs associated with translating and applying new table question answering datasets. By leveraging the characteristics of table data, we suggest translating only the question data of the question answering table datasets into multiple languages. To minimize the performance degradation caused by using the native table data of the original dataset and not directly utilizing the table data in the target language, we applied adaptation methods using contrastive learning and adversarial training. To enhance the reasoning capabilities required for table question answering without diminishing performance in languages other than the one used for pre-training, we further pre-trained a RoBERTa-based multilingual encoder on table data using SQL data. The proposed cross-lingual adaptation demonstrated performance improvements in most of the languages evaluated. Additionally, the pre-training using SQL data significantly improved performance across all languages, even though the pre-training table data consisted of English data.

关键词： Training Degradation Structured Query Language Adaptation models Translation Costs Contrastive learning Question answering (information retrieval) Cognition Multilingual

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Multilingual Table Question Answering for Low-Resource Languages via Selective Question Translation and Cross-Lingual Adaptation

Multilingual Table Question Answering for Low-Resource Langu...

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2025 IEEE International Conference on Big Data and Smart Computing, BigComp 2025

作者： Cho, Sanghyun Kim, Minho Kim, Hye-Lynn Lee, Jung-Hun Kwon, Hyuk-Chul Pusan National University Department of Information Convergence Engineering Busan Korea Republic of Korea Maritime and Ocean University Division of Artificial Intelligence Engineering Busan Korea Republic of R&D Center AI LAB HDC LABS Seoul Korea Republic of Dong-Eui University Department of Artificial Intelligence Busan Korea Republic of Pusan National University Department of Computer Science and Engineering Busan Korea Republic of

关键词： Contrastive Learning

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Boyuan Peng Yiyang Liu Xin Zhu Jie Huang Wenwen Wang Li Fang Qin Zhou Graduate Department of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Aizu-Wakamatsu Fukushima Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness.

关键词： Image segmentation Ultrasonic imaging Accuracy Manuals Speckle Semisupervised learning Robustness Data models Optimization Medical diagnostic imaging

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： Cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

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An end-to-end pipeline for automated scoliosis diagnosis with standardized clinical reporting using SNOMED CT

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Scientific Reports 2025年第1期15卷 1-13页

作者： Shahid, Abdullah Kim, Jeonghoon Byon, Shi Sub Hong, SungHyuk Lee, Inyong Lee, Byoung-Dai AI Lab Healthhub Co. Ltd Seoul 04175 South Korea Division of AI and Computer Engineering Kyonggi University Suwon 16227 South Korea

Recent advancements in automated scoliosis diagnosis using X-ray imaging have focused on detecting spinal curvature through angle measurements, but they often lack systematically standardized clinical terminology schemes. To address these limitations, we propose an end-to-end pipeline that detects individual vertebrae and Cobb angles including reports of structured medical terms. The performance of the proposed pipeline was evaluated on the SpineWeb public dataset, achieving state-of-the-art results with a Circular Mean Absolute Error of 3.50 and Symmetric Mean Absolute Percentage Error of 7.35, outperforming existing methods in both accuracy and efficiency. The results are defined and reported using SNOMED CT-based templates to improve data quality and facilitate efficient secondary use. We have designed our solution to perform large-scale data analysis and inter-hospital comparisons, which can positively impact the healthcare of spinal scoliosis patients. © The Author(s) 2025.

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Classification of IoT Device Communication Through Machine Learning Techniques 1

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1st Eai International Conference on Forthcoming Networks and Sustainability in the IoT Era, FoNeS 2020

作者： Ahmad, Sheraz Prasad, K. N. R. Surya Vara Ullah, Zaib Mostarda, Leonardo Al-Turjman, Fadi Computer Science Division University of Camerino Camerino62032 Italy Department of Electrical and Computer Engineering University of British Columbia Columbia Canada Artificial Intelligence Department Near East University Nicosia Mersin 10 Turkey Research Center for AI and IoT Near East University Nicosia Mersin 10 Turkey

ISBN: (数字)9783030694319

ISBN: (纸本)9783030694302

The Internet of Things (IoT) also called the Internet of Everything is a system of smart interconnected devices. The smart devices are uniquely identifiable over the network and perform autonomous data communication over the network with or without human-to-computer interaction. These devices have a high level of diversity, heterogeneity, and operates with various computational capabilities. It is highly necessary to develop a framework that allows to classify the devices into different categories from effective management, security, and privacy perspectives. Various solutions such as network traffic analysis, network protocols analysis, etc. have been developed to solve the problem of device classification. The signal of a device is an important feature that could be utilized to classify various network devices. We propose a framework to identify network devices based on their signal analysis. We have developed a training data set, by collecting signals from various Wi-Fi and Bluetooth devices in a specific geographic area. A machine learning-based model is proposed for the prediction of network device classification (e.g., a Wi-Fi or Bluetooth device) with 100% accuracy. Furthermore, clustering techniques are applied to the acquired signals to predict the total number of active Wi-Fi devices in a given region. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

关键词： Infrastructure as a service (IaaS)

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MatterChat: A Multi-Modal LLM for Material Science

arXiv

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arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–ai interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-ai interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Structure (composition)

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SmartFit: ai-Powered Training Recommendations with Biometric Integration

SmartFit: AI-Powered Training Recommendations with Biometric...

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Global Conference for Advancement in Technology (GCAT)

作者： Firoj Parwej Suganya A Burri Naresh Veer Sudheer Goud S. Kaliappan G. Manikandan Department of Computer Science and Engineering Mandsaur University Daulatpura Mandsaur Madhya Pradesh India Department of Information Technology Karpagam College of Engineering Coimbatore Tamilnadu India Department of CSE -CS CVR College of Engineering Hyderabad Telangana India Deptartment of AI&DS St. Martin's Engineering College Secunderabad India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This research introduces SmartFit, a state-of-the-art technology that seamlessly integrates biometric data and ai-powered training recommendations. SmartFit monitors the user's pulse rate, oxygen saturation, and muscle activity in real time. Subsequently, it implements machine learning algorithms to develop customized training regimens. This ongoing adjustment to the user's evolving fitness levels and objectives guarantees that the workout regimens are both highly effective and customized to their unique requirements. The proposed system is designed to optimize exercise efficacy, promote extended engagement in fitness activities, and improve user satisfaction. The novel method significantly enhanced the fitness ratings and overall satisfaction of participants in comparison to more conventional forms of exercise, as evidenced by a three-month pilot study. The results demonstrate the potential for SmartFit's data-driven, individualized training recommendations to transform the fitness industry.

关键词： Training Biometrics Machine learning algorithms Transforms Machine learning Muscles Real-time systems User experience Monitoring Optimization

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Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Model

arXiv

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arXiv 2024年

作者： Cho, Seonhee Kim, Choonghan Lee, Jiho Chilkunda, Chetan Choi, Sujin Yoon, Joo Heung Kim Jaechul Graduate School of AI KAIST Gyeonggi Seongnam Korea Republic of Moon Soul Graduate School of Future Strategy KAIST Daejeon Korea Republic of Department of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Chemical Engineering Carnegie Mellon University PittsburghPA United States Division of Environmental Science and Engineering POSTECH Gyeongbuk Pohang Korea Republic of Division of Pulmonary Allergy Critical Care and Sleep Medicine Department of Medicine University of Pittsburgh PittsburghPA United States

Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt. This progress is particularly relevant to the medical domain, where the quality and sensitivity of data pose unique challenges for model training and application. However, the dependency on high-quality data for effective in-context learning raises questions about the feasibility of these models when encountering with the inevitable variations and errors inherent in real-world medical data. In this paper, we introduce MID-M, a novel framework that leverages the in-context learning capabilities of a general-domain Large Language Model (LLM) to process multimodal data via image descriptions. MID-M achieves a comparable or superior performance to task-specific fine-tuned LMMs and other general-domain ones, without the extensive domain-specific training or pre-training on multimodal data, with significantly fewer parameters. This highlights the potential of leveraging general-domain LLMs for domain-specific tasks and offers a sustainable and cost-effective alternative to traditional LMM developments. Moreover, the robustness of MID-M against data quality issues demonstrates its practical utility in real-world medical domain applications. © 2024, CC BY.

关键词： Cost effectiveness

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