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共 27256 条记录，以下是381-390 订阅

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Intelligent prediction of trademark registration appeal outcomes based on natural language processing and CatBoost algorithm 5

Intelligent prediction of trademark registration appeal outc...

引用

5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Zheng, Qinyuan Shanghai Maritime University Shanghai 201306 China

ISBN: (纸本)9781510691667

With the rapid development of artificial intelligence technology, its application in the field of trademark registration applications can save a significant amount of time and human resources. However, research on predicting trademark opposition and review is still relatively limited. This paper proposes a trademark appeal outcome prediction model based on natural language processing and machine learning. The model extracts text features using the TF-IDF algorithm, calculates the similarity between trademark names using the Levenshtein distance, and models the comprehensive features based on the CatBoost algorithm to predict the results of trademark registration appeals. Experiments show that the model achieves an accuracy rate of 69% in predicting trademark opposition and review outcomes, effectively evaluating the similarity between trademark texts, providing auxiliary decision-making for trademark applicants, reducing application time costs, and at the same time, increasing the efficiency of relevant institutions. © 2025 SPIE.

关键词： Catboost algorithm Levenshtein distance prediction model TF-IDF algorithm Trademark rejection review prediction

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RF Distillation Diffusion Model: An Efficient RFF Data Augmentation Method

RF Distillation Diffusion Model: An Efficient RFF Data Augme...

引用

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

作者： Lin, Kai Zhao, Caidan Chen, Jingqian Xiao, Liang The Institute of Artificial Intelligence China School of Informatics Xiamen University China School of Electrical Engineering Xi'an Jiaotong University China

ISBN: (纸本)9798350368741

Radio Frequency Fingerprint (RFF) based physical layer authentication technology provides enhanced security for wireless communications. However, the spatiotemporal overlap of wireless signals makes it challenging to label wireless device samples. Moreover, generative networks, such as Generative Adversarial Networks (GANs) struggle to retain the subtle signal features. Utilizing existing unlabeled samples poses a significant challenge for RFF data augmentation. This paper proposes the RF Distillation Diffusion (RFDD) model, an efficient method for RFF data augmentation. RFDD employs a conditional diffusion model to generate high-quality RF signals, which fully utilizes existing unlabeled samples to learn the data distribution of signals, and labeled samples are used to enhance the capability of RFF feature extraction. Additionally, knowledge distillation is utilized to improve sampling efficiency. Experimental results show that the RFDD can fully use 20% unlabeled samples to generate high-quality synthetic RF signals within only 0.45 seconds and improve RFF identification accuracy by 25.63% with 40% synthetic signals under SNRs from -5 to 5 dB. The code is available at https://***/XMU-Kai/RFDD. © 2025 IEEE.

关键词： data augmentation guided diffusion models knowledge distillation radio frequency fingerprinting

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Ethics in natural language processing: Addressing Bias, Privacy, and Misinformation

Ethics in Natural Language Processing: Addressing Bias, Priv...

引用

Pervasive Computational Technologies (ICPCT), international conference on

作者： Himanshu Sanjay Joshi Hamed Taherdoost College of Business Westcliff University CA USA R&D Department Hamta Business Corporation Vancouver Canada College of Technology & Engineering Westcliff University CA USA University Canada West Vancouver Canada GUS Institute | Global University Systems London UK Q Minded | Quark Minded Technology Inc. Vancouver Canada

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

natural language processing (NLP) stands at the forefront of artificial intelligence, empowering machines with human-like capabilities to process text and speech. The versatile applications of NLP encompass information retrieval, text summarization, text categorization, and machine translation. In the dynamic landscape of information dissemination, the prevalence of misinformation, bias and the need for credible sources have become critical concerns. This research effort addresses critical ethical considerations related to the development of NLP algorithms using the Value Sensitive Design (VSD) framework. This paper explores the intersections between NLP and knowledge graphs, through the lens of VSD aiming to enhance the ethics of NLP solutions.

关键词： Ethics Privacy Computational modeling Text categorization Text summarization knowledge graphs natural language processing Fake news Artificial intelligence Speech processing

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Advancing Multilingual Sentiment Understanding with XGBoost, SVM, and XLM-RoBERTa 5th

Advancing Multilingual Sentiment Understanding with XGBoost,...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Gaikwad, Arya Belhekar, Pranav Kottawar, Vinayak D.Y. Patil College of Engineering Pune India

ISBN: (纸本)9789819780426

In this comprehensive research, we explore multilingual sentiment analysis, aiming to classify sentiments as positive, negative, or neutral across languages. Our dataset includes 192,839 examples in English, Japanese, German, French, Spanish, Chinese, Indonesian, and Hindi. We assess three models: XGBoost (62% accuracy), SVM (64.2% accuracy), and the multilingual XLM-RoBERTa-base, which excels with an impressive 78.37% accuracy. XLM-RoBERTa-base’s adaptability to diverse languages makes it a crucial asset in multilingual sentiment analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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Research on Intelligent Management and Assisted Decision Making Technology of Electric Power Research Enterprise Based on knowledge engineering 3

Research on Intelligent Management and Assisted Decision Mak...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Zhang, Baoliang Xu, Yinghui Li, Chengyuan Jiang, Yuan Cui, Wenchao Yue, Hongxu Hui, Chen Hu, Ziwei Digitisation Department China Electric Power Research Institute Beijing China School of Mechanical and Electrical Engineering China University of Mining and Technology Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798400712692

Currently, the internal management knowledge of electric power research enterprises is difficult to integrate and structured organization, resulting in greatly reduced decision-making efficiency. knowledge Graph(KG), as an efficient knowledge organization, helps to connect the complex knowledge network. However, for a Q&A platform based solely on knowledge graphs, it can be challenging to interpret and analyze natural language input, especially when considering historical Q&A data. In this paper, we combine the KG with a Large language Model(LLM), choose the ERNIE model fine-tuned by the text of electric power domain knowledge, and use the TransE method to integrate the KG into the model's structure to build a knowledge-based Q&A system that assists researchers in decision-making. The experimental results show that the ERNIE model combined with KG outperforms the comparison model in terms of precision, recall, and F1 value in entity and relation classification tasks, and the performance is further improved by increasing the number of attention heads. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： knowledge Graph embedding knowledge Quizzing Large language Model

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PRESS: Defending Privacy in Retrieval-Augmented Generation via Embedding Space Shifting

PRESS: Defending Privacy in Retrieval-Augmented Generation v...

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

作者： He, Jiaming Liu, Cheng Hou, Guanyu Jiang, Wenbo Li, Jiachen School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Computer Science and Cyber Security Oxford Brookes College Chengdu University of Technology China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350368741

Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems are exposed to substantial privacy risks during the information retrieval process, leading to potential data leakage of private information. In this work, we present a Privacy-preserving Retrieval-augmented generation via Embedding Space Shifting (PRESS), systematically exploring how to protect privacy in RAG systems. Specifically, we first conduct proximal policy optimization (PPO) based training on pre-trained language models to generate target training samples. Then we employ a purposive shift fine-tuning on the text embedding model with the generated samples for guiding the RAG system to map potential privacy leaking queries to safe target in embedding space. Extensive experimental results on representative models and datasets demonstrate that our protection method achieves high defense performance with high efficiency while keeping the normal functionality of the RAG system. © 2025 IEEE.

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PoliticPA 2024: Author Profiling Gender and Political Ideology of Politicians in Panama 10th

PoliticPA 2024: Author Profiling Gender and Political Ideolo...

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10th international conference on Technologies and Innovation

作者： Cedeno-Moreno, Denis Pan, Ronghao Vargas-Lombardo, Miguel Garcia-Diaz, Jose Antonio Garcia-Sanchez, Francisco Univ Tecnol Panama Panama City Panama Univ Murcia Fac Informat Campus Espinardo Murcia 30100 Spain

ISBN: (纸本)9783031757013;9783031757020

Author profiling in computational linguistics and natural language processing identifies traits such as age, gender, education level, and personality from written text, with applications in marketing, cybersecurity, forensics, and academia. Psychographic traits, such as political ideology, enhance the understanding of behavior. Recent initiatives such as PoliticES and PoliticIT have demonstrated the extraction of political ideology and demographic traits from text clusters. Here we present the first version of the PoliticPA 2024 dataset, designed for author profiling tasks on Panamanian politicians. This dataset allows the extraction of gender and political ideology from both binary and multi-class perspectives. We evaluate this dataset using a baseline based on Bag of Words features and logistic regression, as well as fine-tuned versions of several Spanish and multilingual Large language Models and an approach based on mBART. Preliminary results indicate that lightweight models generally outperform others, except for multiclass political ideology. However, the current corpus is limited compared to other political author profiling datasets, making comprehensive comparisons difficult. Future work will include compiling additional tweets and users to expand the dataset, aiming for at least 500 clusters while exploring zero and few-shot learning models.

关键词： Authorship Analysis Author Profiling natural language processing

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Multi-phase difference enhanced fusion for tumor and pancreas segmentation

Multi-phase difference enhanced fusion for tumor and pancrea...

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2024 international conference on Computer Vision and Image processing, CVIP 2024

作者： He, Chongyu Zhou, Fugen Liu, Bo Li, Jiaping Zhou, Dinghua Image Processing Center School of Astronautics Beihang University Beijing100191 China Department of Interventional Oncology The First Affiliated Hospital Sun Yat-Sen University Guangdong Guangzhou China Department of Hepatobiliary Surgery PLA Rocket Force Characteristic Medical Center Beijing China

ISBN: (纸本)9781510688384

Multi-phase contrast-enhanced computed tomography (CECT) has been shown to effectively refine the segmentation accuracy for both tumors and organs. Nevertheless, enhancing the segmentation accuracy of the pancreas and pancreatic tumors using multi-phase CECT remains a challenge, which is crucial for subsequent clinical analysis and treatment. Current methods either simply concatenate multi-phase features along the channel direction or utilize attention mechanisms to perform weighted summation of features across phases, neglecting the contribution of phase-specific difference features to segmentation accuracy. In view of this, this paper proposes a multi-phase segmentation method for the pancreas and pancreatic tumors, improving segmentation accuracy by enhancing the phase-specific difference features. Additionally, we propose a training strategy to improve the segmentation accuracy of model when only single-phase CECT is available. The process achieved by distilling the learned knowledge of a segmentation model trained on multi-phase CECT. The proposed method and training strategy proved effective through experiments conducted on a privately collected multiphase CECT dataset of pancreatic tumors. © 2025 SPIE.

关键词： Computerized tomography

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VedicFusion: Bridging Ancient Wisdom and Modern AI Through natural language processing 3

VedicFusion: Bridging Ancient Wisdom and Modern AI Through N...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kasundra, Chintan Kariya, Shreekumar Dhaduk, Meet Rupda, Khushi Vachhani, Griva Jain, Rituraj Palaniappan, Damodharan Marwadi University Department of Information Technology Gujarat Rajkot India

ISBN: (纸本)9798331523893

Vedic texts are wise and profound but hard to comprehend for modern readers. Advancements in AI, particularly in NLP, have presented a new opportunity to interpret complex texts. This paper introduces the concept of VedicFusion, a system that combines ancient Vedic wisdom with modern AI. Utilizing transformer-based models and a huge database of Vedic text, VedicFusion creates an interface for interpreting and exploring Vedic teachings. This tackles issues of cultural authenticity and contextual understanding. VedicFusion unlocks ancient wisdom, aiding in philosophical investigation, providing moral direction, and encouraging spiritual development. Its relevance supports AI work and research into ancient knowledge combined with modern technology in an accountable manner. Results show evidence of an increased perception and accessibility of the new teachings that have been derived because of these questions. Development of the database will expand while models for NLP refinement. © 2025 IEEE.

关键词： AI Ethics Ancient Wisdom Digital Humanities Large language Models natural language processing

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Enhancing Mental Wellbeing Among College Students Using Autogen

Enhancing Mental Wellbeing Among College Students Using Auto...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Reddy, Chilukuri Riktha Devi, B. Madhavi Reddy, Kura Pranav Medarametla, Lahari Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529833

Mental health support is crucial for students, who are vulnerable to challenges due to academic pressures and social expectations. University culture often fosters misconceptions about mental well-being, leading to stigma that discourages students from seeking help. This project introduces an AI Therapist - a conversational agent offering a private, non-judgmental space for students to discuss personal challenges. Using Sentiment Analysis, a natural language processing (NLP) method powered by machine learning, the system evaluates the emotional tone of these interactions. Through this analysis, the AI provides insights into students' mental health over time, visualized through data reports generated with tools like matplotlib. These reports allow students to track their emotional journey and promote self-awareness. The goal is to create an accessible support system that encourages self-reflection, reduces the stigma around mental health, and fosters early intervention. © 2025 IEEE.

关键词： Students

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