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Federated Prototype Guided Adaption for Vision-language Models

Federated Prototype Guided Adaption for Vision-Language Mode...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, Youchao Huang, Dingjiang School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Federated Learning (FL) is a new pivotal paradigm for decentralized training on heterogeneous data. Recently fine-tuning of Vision-language Models (VLMs) has been extended to the federated setting to improve overall performance. Unfortunately, in this case, FL still faces two critical challenges that hinder its actual performance: data distribution heterogeneity and high resource costs brought by large VLMs. In this paper, we introduce FedPGA, a prototype-guided method, for achieving performance improvements in the federated setting for VLMs. Concretely, we design a prototype-based adapter for the vision-language model, CLIP. The lightweight adapter updates the prior knowledge encoded in CLIP to enhance its adaption capability further and avoid the effects of data distribution heterogeneity in the federated setting. Simultaneously, small-scale operations can mitigate the computational and communication burden caused by large VLMs. Our comprehensive empirical evaluations of nine diverse image classification datasets show that our method is superior to existing FL methods under VLMs. © 2025 IEEE.

关键词： federated learning few-shot classification prototype-based adapter vision-language model

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Revolutionizing Medical Inquiry Responses Harnessing Advanced language Models for Enhanced Precision 2

Revolutionizing Medical Inquiry Responses Harnessing Advance...

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2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Keerthana, S.M. Kirthidevi, S. Nandhana, V. St. Joseph's Institute of Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331534950

In the swiftly advancing domain of healthcare informatics, the exigency for intricately sophisticated and meticulously calibrated information retrieval systems has attained a critical juncture. This paper elucidates the development of an avant-garde question-answering (QA) system, meticulously engineered to tackle the multifaceted challenges of the healthcare sector through the deployment of advanced natural language processing (NLP) methodologies. The system incorporates BioBERT a specialized refinement of the BERT architecture, extensively fine-tuned on an expansive biomedical corpus to proficiently parse, interpret, and resolve complex medical inquiries with unparalleled precision and contextual acuity. Central to its innovation is an advanced attention mechanism that dynamically prioritizes pivotal components of a query, thereby substantially augmenting its capacity to extract highly specific and contextually relevant information from a vast medical knowledge repository. This methodological advancement adeptly addresses the entrenched limitations of contemporary QA systems, which often grapple with the specificity and complexity of medical content, thereby markedly enhancing response efficacy and reliability. Positioned to revolutionize clinical decision-making, elevate patient outcomes, and expedite research endeavours, this system provides considerable utility to clinicians, patients, and researchers. The paper accentuates the transformative impact of amalgamating BioBERT with sophisticated attention mechanisms, establishing a novel benchmark in medical QA systems that is both technically robust and exquisitely attuned to the nuanced demands of the healthcare sector. © 2024 IEEE.

关键词： natural language processing systems

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Identifying The Established Semantic Kinds In Text Using Named Entity Recognition

Identifying The Established Semantic Kinds In Text Using Nam...

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2024 international conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Seershika, Mitnasala Aishwarya, Vasuri Vani, V. Karthik, N. National Institute of Technology Puducherry Department of Computer Science of Engineering Puducherry Karaikal609 609 India

ISBN: (纸本)9798331540685

Named entity recognition (NER) is a natural language processing (NLP) method that extracts information from text. NER includes identifying and ordering significant data in text known as named elements. By approaching span-based method (where tokens are represented as spans) we can detect named entity for each span. However some current algorithms check every span separately without knowing previous span, which leads to ambiguity. To overcome this, a bi-directional two-dimensional operation is used, which uses knowledge of previous spans and compares the two spans and generates meaningful named entities. For implementation of the NER we have used the three public datasets. The experimental results in this paper shows that the method we used is very effective to generate meaningful nested named entities. © 2024 IEEE.

关键词： Named Entity Recognition Planarized sentence Self-Encoding

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Enhancing Abstractive Text Summarization with Proximal Policy Optimization 4

Enhancing Abstractive Text Summarization with Proximal Polic...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Reddy, K. Lokeshwar Phani Shanmukh, M. Charan Kumar, M. Kumar M, Tharun Arjun Kumar, C. Prasanna Kumar, R. Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

In the realm of natural language processing (NLP), Abstract Text Summarization (ATS) holds a crucial position, involving the transformation of lengthy textual content into concise summaries while retaining essential information. This research paper delves into the utilization of advanced algorithms such as Seq-Seq Transfer Learning, Pegasus, and Proximal Policy Optimization for the abstraction text summarization process. Specifically, this study incorporates cutting-edge techniques like deep neurotemporal models and Reinforcement Learning (RL), with a focus on Preorder language Models (PTLM). The integration of knowledge into the development process further enhances the efficacy of ATS applications. The paper meticulously examines the recent advancements in abstract concept extraction, addressing both historical and existing challenges while proposing innovative solutions. Emphasizing the significance of data and metrics in abstract ATS, this research incorporates the widely used ROUGE metric for evaluation. By comparing influential models, the study provides a comprehensive overview of the field's evolution. In addition, it explores the integration of transformative graph-based Transformer architecture and PTLM, which have revolutionized NLP. The research paper concludes by offering valuable insights into the future of abstract text summarization, paving the way for further exploration and innovation in the domain. © 2024 IEEE.

关键词： Abstract Text Summarization DQN Algorithm natural language processing Pegasus Algorithm Seq-Seq Algorithm Transfer Learning

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Read Right: Empowering the Blind to Read the World

Read Right: Empowering the Blind to Read the World

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2024 IEEE international conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Prasuna, Vempati Lakshmi Fathimabi, Sk. Adusumilli, Apoorva Rajesh, Bandaru Veera Venkata Velagapudi Ramakrishna Siddhartha Engineering College Information Technology Vijayawada India

ISBN: (纸本)9798350385199

In the real world, knowledge comes from books and papers. Now that information only reaches to those with clear vision. In the community there are a part of people suffering either from poor eyesight or blindness. Braille is one of the methods employed to interpret several reports or books, however obtaining all these files in braille may be prohibitively expensive as well as regularly impossible. Hence, considering these issues and flaws in existing systems this paper is designed as a simple user-friendly mobile application 'Read Right' that works on voice commands like 'Take Picture' to click an image of the document which extracts text from picture as well plays it out (audio) also providing futuristic object detection. All this is designed using Firebase ML kit which predominantly uses bitmap class for preprocessing, text-to-speech synthesizer and FirebaseVisionObjectDetector, for joining machine learning capabilities into portable applications. Hence, the application stands out for its flexibility in dealing with official archives through voice. © 2024 IEEE.

关键词： natural language processing systems

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ChatGPT: An Advanceds natural language processing System for Conversational AI Applications—A Comprehensive Review and Comparative Analysis with Other Chatbots and NLP Models 8th

ChatGPT: An Advanceds Natural Language Processing System for...

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8th international conference on ICT for Sustainable Development, ICT4SD 2023

作者： Keshamoni, Kumar Assistant Professor Department of ECE Vaagdevi Engineering College Warangal India

ISBN: (纸本)9789819956517

ChatGPT is an OpenAI-developed big-language model architecture based on the GPT-3.5 standard. It is aimed at engaging users in natural language talks on a variety of themes. With its comprehensive knowledge of English and the world, input from users can result in replies that resemble those of humans. It can understand and react to a variety of linguistic patterns and idioms because it was trained on a vast corpus of Internet content. Numerous programmes, such as customer support systems, virtual assistants, and chatbots have already implemented ChatGPT. Potentially useful for communication, education, and entertainment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： natural language processing systems

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Using Large language Models to Facilitate the Utilization of Specific Application Programming Interfaces in Learning Factories 14th

Using Large Language Models to Facilitate the Utilization of...

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14th international conference on Learning Factories (CLF)

作者： Dorka, Frithjof El Otmani, Kaoutar Hentsch, Maximilian Kunster, Nils Palm, Daniel Reutlingen Univ ESB Business Sch Alteburgstr 150 D-72762 Reutlingen Germany Fraunhofer Inst Mfg Engn & Automat Alteburgstr 150 D-72762 Reutlingen Germany

ISBN: (纸本)9783031653995;9783031654008

Technologies related to Industry 4.0, such as the Internet of Things (IoT) and Artificial Intelligence (AI), find increasing applications in manufacturing systems. However, the technical implementation of IoT-based or AI-based solutions requires interaction and information exchange between the various components of complex information processing systems. Students of interdisciplinary study programs, such as industrial engineering, often possess conceptual yet isolated knowledge of manufacturing systems, IT infrastructure, and information processing without proficiency regarding application programming interface (API) usage. However, APIs are paramount for enabling the interaction of individual components of complex information processing systems. Unfortunately, adapting the general descriptions in API documentation to a student's specific application is often challenging, hindering a comprehensive hands-on learning experience for students training on implementing applications into manufacturing systems of learning factories. Therefore, this paper proposes a novel approach for leveraging Large language Models (LLMs) to facilitate the utilization of APIs for students' hands-on training on implementing applications and the respective information processing within the context of manufacturing systems and learning factories. The proposed approach comprises an LLM extended using context data specific to the employed test API and enables user interaction via a natural language dialogue-based chat interface.

关键词： LLM API Manufacturing Systems Learning Factory

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FinanceQA-Agent: A High-Precision Comprehensive Financial Technology Question-Answering Intelligent System Based on Vector Databases 5

FinanceQA-Agent: A High-Precision Comprehensive Financial Te...

引用

5th international conference on Advances in Electrical engineering and Computer Applications, AEECA 2024

作者： Liu, Xin Zhu, Jingjing Southeast University School of Instrument Science and Engineering Nanjing China Bank of China Information Technology Operations Center Hefei China

ISBN: (纸本)9798350355253

In response to the escalating complexity of financial operations and the pressing need for intelligent decision support, FinanceQA-Agent emerges as an avant-garde Financial Technology (FinTech) solution. This system integrates state-of-the-art natural language processing with advanced vector encoding to craft a high-precision, robust question-answering platform. Utilizing intranet isolation and load balancing, it ensures unparalleled security and stability, while its meticulously designed knowledge base and prompt engineering elevate the conversational accuracy and naturalness. The research methodology encompasses a multifaceted approach, including the deployment of large language models and semantic vector computations, underpinned by a comprehensive system architecture that facilitates scalability and maintenance. Pivotal to our findings is the system's remarkable capability to distill and generate precise financial insights, thereby significantly enhancing the FinTech domain's intelligence and operational efficiency. In conclusion, FinanceQA-Agent represents a transformative stride in FinTech, propelling the industry towards a future characterized by exceptional intelligence, efficiency, and security through its sophisticated question-answering prowess. © 2024 IEEE.

关键词： Semantics

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Transforming Multi-Document Abstractive Summarization: A Comparative Study of State-of-the-Art Models and Benchmarks

Transforming Multi-Document Abstractive Summarization: A Com...

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2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Motghare, Manish Agrawal, Avinash Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517953

This study presents a comprehensive comparative analysis of state-of-the-art models for multi-document abstractive summarization, focusing on their performance across key datasets, including MultiNews, WikiSum, DUC, and CNN/Daily Mail. Given the rapid proliferation of text data, the ability to generate coherent and succinct summaries from multiple documents holds immense potential for knowledge dissemination, research acceleration, and efficient information retrieval. To achieve this goal, we meticulously evaluated prominent models such as BART, Pegasus, T5, Longformer, Pointer-Generator Networks, and more. Extensive experimentation involved fine-tuning each model for optimal performance using benchmark datasets, measuring their effectiveness through ROUGE-1, ROUGE-2, ROUGE-L scores, and additional precision metrics. Results indicate that PRIMERA outperformed its counterparts, achieving the highest ROUGE scores across the datasets, demonstrating superior summarization quality and coherence. The findings underscore the nuanced capabilities of transformer-based architectures compared to hierarchical and graph-based approaches, offering insights into scalability and generalization for multi-document tasks. This research reflects rigorous data processing and performance tuning, leveraging a custom dataset creation pipeline and deep analysis of generated summaries. Our work advances the field by identifying optimal strategies and architectures for multi-document summarization, paving the way for future innovations and practical applications in natural language processing. © 2024 IEEE.

关键词： natural language processing systems

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Artificial Intelligence in the Workplace: Revolutionizing Information Capture and Retrieval 15

Artificial Intelligence in the Workplace: Revolutionizing In...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Haripriya, V. Faujdar, Pramod Kumar Jaffar, A. Mohamed Nivedha, C.S. Thaware, Shailesh J. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Mechanical Engineering Jaipur India Atlas SkillTech University Department of Isme Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The arrival of synthetic Intelligence (AI) in the place of job has delivered progressive adjustments in the way organizations capture and keep information. AI era has superior to allow automated data capture for both traditional and dynamic sorts of facts, from emails to documents to conversations. AI-powered algorithms can automatically extract, categorize, and keep records, allowing get admission to relevant statistics quickly and accurately. AI also can be used to automate retrieval of data with natural language processing able to knowledge complicated queries for quick and correct effects. AI-pushed technology are reworking the manner agencies seize and shop statistics, simplifying search and retrieval and deepening the skills of statistics analytics. AI is ushering in an era of smarter, greater efficient knowledge control for the current place of business. © 2024 IEEE.

关键词： algorithms conversations knowledge reworking simplifying

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