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共 27256 条记录，以下是251-260 订阅

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language-Queried Target Sound Extraction Without Parallel Training Data

Language-Queried Target Sound Extraction Without Parallel Tr...

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

作者： Ma, Hao Peng, Zhiyuan Li, Xu Li, Yukai Shao, Mingjie Kong, Qiuqiang Liu, Ju School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University NC United States ARC Lab Tencent PCG China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability. © 2025 IEEE.

关键词： contrastive language-audio pre-training retrieval-augmented strategy target sound extraction uni-modal training for multi-modal tasks

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An Analysis of Various Classification Algorithms for Fake News Detection 2

An Analysis of Various Classification Algorithms for Fake Ne...

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2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Khare, Sakshi Singh, Pushpa Kumar, Pankaj Dept. of Computer Science and Engineering G. L. Bajaj Institute of Technology & Management Greater Noida India

ISBN: (纸本)9798331530389

With the expansion of social media and advanced stages, the spread of fake news has ended up a noteworthy societal issue. This paper presents a comprehensive outline of machine learning strategies utilized for the detection of fake news. We talk about different approaches such as natural language processing (NLP), unsupervised learning calculations, and machine learning models utilized within the recognizable proof of fake news. We dive into highlight designing, opinion investigation, and organize examination as key components of fake news detection frameworks. Moreover, we look at datasets, assessment measurements, and challenges related with detection of fake news. Through this analysis, we point to supply bits of knowledge into the current state-of-the-art techniques and headings for future investigate in combating fake news dispersal. This paper mainly used K-Nearest Neighbor, Jaccard similarity, LSTM, Decision Tree, and Logistic regression Classifiers for the classification. Main objective of this paper to verify the accuracy of models by performing the pre-processing the data and handle the unbalanced data more appropriately. The results of this papers show comparison of all classifiers with accuracy. Our results shows that KNN achieves 83%, 84% for Logistic Regression, 84% for Decision Tree, 85% for Jaccard Similarity & Random Forest is 85% and 98% for LSTM in our experiment. © 2025 IEEE.

关键词： Adversarial machine learning

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Uniandes at the Regulations Challenge Task: A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts 9

Uniandes at the Regulations Challenge Task: A Scalable Frame...

引用

Joint Workshop of the 9th Financial Technology and natural language processing, FinNLP 2025, the 6th Financial Narrative processing, FNP 2025, and the 1st Workshop on Large language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Carrión, Santiago Martínez Castañeda, Juan Manuel Manrique, Rubén Francisco Dept. of Systems and Computing Engineering Universidad de los Andes Bogotá Colombia

ISBN: (纸本)9798891762091

This study presents a comprehensive approach to developing a domain-specific large language model (LLM) for regulatory and financial text interpretation. A specialized corpus was constructed through large-scale scraping of financial and regulatory documents across domains such as compliance, licensing, and financial reporting. The data was preprocessed using GPT-4o-mini with prompt engineering to retain critical information and remove noise. We further pre-trained a LLaMA-3.1-8B model on the curated corpus and fine-tuned it using an instruction dataset covering nine tasks from the Coling 2025 Regulations Challenge (Wang et al., 2024), including acronym expansion, regulatory question-answering, and XBRL-based financial analytics, employing QLoRA to reduce memory requirements. The model exhibits a slight improvement from baseline answering complex regulatory questions (detailed QA) and expanding acronyms. This study demonstrates the potential of domain-specific LLMs in regulatory text interpretation and lays the groundwork for future research in specialized NLP evaluation methodologies. © 2025 Association for Computational Linguistics.

关键词： Finance

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Role-Specific Reward Design with Large language Model for StarCraft II

Role-Specific Reward Design with Large Language Model for St...

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

作者： Li, Sijia Luo, Haonan Zhang, Xu Zeng, Xin Shen, Zhixuan Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Nanjing University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350368741

Reward acts as a signal to guide the agent's learning process in Reinforcement Learning (RL), evaluating and assigning rewards to the agent's actions based on their alignment with goals. Designing reward is challenging in multi-agent environment such as StarCraft II benchmark since agents face credit allocation and role adaptation problems. Recent studies have successfully exploited the language understanding and reasoning capabilities of large language models (LLMs) to learn manipulation tasks. Impressed by the remarkable power of LLMs, this paper employs LLMs as role-specific reward designer for playing StarCraft II, making rewards more flexible and task-oriented. Firstly, we develop an interactive text and multi-agent RL environment to study real-time strategy generation in StarCraft II. Secondly, we use LLMs to interpret the game situation and understand agent roles from user instructions. Then, by assigning appropriate subtasks, LLMs quantify the completion of these subtasks to generate role-specific rewards. Further, credit assignment problem is addressed by introducing dynamic reward weights in value decomposition method. In StarCraft II maps, experiments show that role-aligned RL agents trained with our framework achieve superior policy performance, and win rate results demonstrates the effectiveness of our approach in decision-making for micromanagement and long-term planning. © 2025 IEEE.

关键词： Large language Models Multi-agent Reinforcement Learning Reward design StarCraft II Value decomposition

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Conversational Symptom Checker Chatbot for Disease Prediction and Suggesting Nearby Hospitals using Machine Learning and Location Services 3

Conversational Symptom Checker Chatbot for Disease Predictio...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Burle, Rushikesh Gundewar, Swapnil Gaurkhede, Sanskruti Fulkar, Bhushan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331509675

A chatbot, sometimes known as a conversational agent, is software that uses natural language to interact with people. Travelling to and from a hospital for treatment takes a lot of time for people with hectic schedules, which makes many of them disregard their health issues. People frequently postpone going to the hospital for small problems that might develop into serious illnesses. For these people, interacting with chatbots to get helpful medical information is a cost-effective, time-efficient, and convenient option. This paper suggests a framework for a machine learning-based chatbot that combines natural language, machine learning, and statistical analysis. gaining knowledge of the technologies in a Spark cluster to help create an intelligent chatbot. In this work, we conducted a thorough analysis of current literature, looking at a large number of chatbot-related papers over the last five years. Next, we introduced many related tasks to our chatbot, which uses user symptoms to predict diseases and, depending on the user's location, recommends hospitals in the area. Users will be able to easily engage with the chatbot using the system's online interface. © 2025 IEEE.

关键词： Chatbots

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GPT-C: Generative PrompT Compression

GPT-C: Generative PrompT Compression

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

作者： Liu, Lijun Wang, Rui Jing, Lihua Lv, Feixiao Zhu, Zixuan Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Large language models (LLMs) increasingly rely on lengthy prompts to achieve complex tasks. However, longer prompts not only increase inference costs but pose significant challenges for limited context windows of LLMs. Prompt compression aims to accelerate inference in long-text scenarios while preserving performance in various tasks. Conventional extractive compression methods often lead to semantic incoherence and loss of primitive key information. To this end, we propose a Generative PrompT Compression (GPT-C) paradigm. In the absence of appropriate datasets, we design a Collaborative Ordered Agent (Co-Agent) framework to distill high-quality datasets. We then develop a task-agnostic, length-adaptive reinforcement learning strategy that controls compression length and enhances the generalization across diverse tasks. Extensive experiments demonstrate that GPT-C achieves performance comparable to the original prompts while utilizing only 20% of the tokens. © 2025 IEEE.

关键词： Large language models Prompt compression

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6th international conference on knowledge Graphs and Semantic Web, KGSWC 2024

6th International Conference on Knowledge Graphs and Semanti...

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6th international conference on knowledge Graphs and Semantic Web, KGSWC 2024

ISBN: (纸本)9783031812200

The proceedings contain 23 papers. The special focus in this conference is on knowledge Graphs and Semantic Web. The topics include: Towards Complex Ontology Alignment Using Large language Models;enhancing knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metrics;views, Semantic Data Catalog and Role-Based Access Control for Ontologies and knowledge Graphs;accelerating Medical knowledge Discovery Through Automated knowledge Graph Generation and Enrichment;ontoSeer - A Recommendation System to Improve the Quality of Ontologies;Adaptive Planning on the Web: Using LLMs and Affordances for Web Agents;Enriching RDF Data with LLM Based Named Entity Recognition and Linking on Embedded natural language Annotations;TEDME-KG Metrics Framework: A Metrics Framework for TEmporal Data Modelling Evaluation in knowledge Graphs;SFARDE: A knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of Ontologies;leveraging Graph Models for Comprehensive Visual Analytics of Equine Heritage;OWL2Vec4OA: Tailoring knowledge Graph Embeddings for Ontology Alignment;Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF and Smart Energy Appliances;YOKO ONtO: You only KNIT One Ontology;CoKGLM: Detecting Hallucinations Generated by Large language Models via knowledge Graph Verification;autOnto: Towards A Semi-Automated Ontology engineering Methodology;Enhancing Question Answering Systems with Generative AI: A Study of LLM Performance and Error Analysis;disjointness Violations in Wikidata;enhancing WebProtégé with Version Control Systems;visual Presentation and Summarization of Linked Data Schemas;a Proposed Ontology Evaluation Tool to Assist Ontology Engineers in Selecting Ontologies During the Reuse Phase;manufacturing Commonsense knowledge.

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Understanding Sarcasm Detection Through Mechanistic Interpretability 4

Understanding Sarcasm Detection Through Mechanistic Interpre...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Katta, Jithendra Allanki, Manikanta Kodumuru, Nikhil Reddy School of Engineering and Applied Sciences The George Washington University WashingtonDC United States

ISBN: (纸本)9798331523923

Detecting sarcasm remains a significant challenge in natural language processing due to the nuanced interplay of context, sentiment, and contradiction. This study investigates sarcasm detection using the BERT-Large model, emphasizing mechanistic interpretability to enhance performance and transparency. A novel interpretability framework is applied, combining SHAP explanations, attention visualizations, and ablation studies, to identify critical components within the BERT architecture. The research reveals that deeper layers and specific attention heads in BERT are instrumental in isolating sarcasm-specific patterns. Through fine-tuning on a sarcasm-labeled Twitter dataset and leveraging clustering techniques, the study demonstrates the hierarchical progression of sarcasm detection across BERT layers. This work contributes to building reliable and interpretable NLP systems for sentiment analysis, with implications for improving sarcasm detection in real-world applications. © 2025 IEEE.

关键词： Ablation

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23rd international conference on Informatics in Economy, IE 2024

23rd International Conference on Informatics in Economy, IE ...

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23rd international conference on Informatics in Economy, IE 2024

ISBN: (纸本)9789819601608

The proceedings contain 39 papers. The special focus in this conference is on Informatics in Economy. The topics include: Performance Evaluation of Data Vault and Dimensional Modeling: Insights from TPC-DS Dataset Analysis;revolutionizing Healthcare: Harnessing natural language processing and Big Data for Predictive Disease Diagnosis;enhancing the Involvement Level of Volunteer Students in the Academic Community: Efficient Usage of Digital Resources;intelligent Models in Power Delivery Management for Economic Damage Computation;smart City Parking Applications in Romania: An Analysis and Conclusions;measurement and Verification Solution for Energy Performance Contracting in the Building Sector;analyzing the Antecedents of Deceived Buying and Disinformation Risk;driving Factors of Social Commerce Intention: The Role of Social Commerce Constructs, Social Influence, and Trust;strategic Collaboration in international University Networks–Case Study;Comparative Analysis of natural language Query Responses on BPMN Model Serializations: RDF Graphs Versus BPMN XML;a Hybrid Retrieval-Augmented Generation Approach for Heterogeneous knowledge Bases;digitalization in Higher Education—Designing and Implementing an Application to Facilitate the University Final Thesis Collaboration Process;an Overlook of IoT Finance and Pre-existing Security Challenges in Financial Transactions via IoT Devices on Blockchain Networks;models for Network Traffic Behavioral Analysis in IoT Systems;augmented Reality in Education—Prototype for the Undergraduate System in Romania;empowering Sustainability: Upcycling Smartphones as the Future of IoT and Edge Computing in Emerging Economies;quantum Computing and Cybersecurity: Threat or Opportunity?;new Real-Time Encryption Mechanism for Financial Transactions;enhanced Blockchain-Based e-Voting System Using Zero-knowledge Proofs;The Environmental Kuznets Curve for Deforestation in Romania: An ARDL-Based Evaluation.

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From Pictures to Prompts: Analyzing and Reconstructing AI-Generated Images with BLIP2 and CLIP 24

From Pictures to Prompts: Analyzing and Reconstructing AI-Ge...

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8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Kuo, Yi-Hsuan Chang, Hsien-Tsung Department of Computer Science and Information Engineering Chang Gung University Taoyuan China Bachelor Program in Artificial Intelligence Chang Gung University Taoyuan China

ISBN: (纸本)9798400717383

This study presents a novel approach to analyzing and reconstructing AI-generated images using BLIP2 and CLIP models, focusing on a dataset of 268,000 Midjourney-generated images and prompts. We introduce a multi-level feature classification system encompassing 11 categories and 1,100 features, and employ BLIP2 for descriptive text generation and CLIP for feature similarity analysis. Our methodology involves image pre-processing, custom feature extraction, and image reconstruction under various conditions. The effectiveness of our approach is demonstrated through similarity scores between original and reconstructed images, ranging from 68.15 to 71.25 across different feature extraction conditions. This research contributes to the fields of natural language processing and information retrieval by offering insights into AI-generated image analysis, proposing new methods for feature extraction and reconstruction, and suggesting avenues for improving image generation models and multi-modal understanding. © 2024 Copyright held by the owner/author(s).

关键词： Sentiment analysis

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