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Comparative Analysis of Contextual AI for Political Tweets 1

Comparative Analysis of Contextual AI for Political Tweets

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Khatavkar, Vaibhav Petkar, Sneha Vaidya, Archana Institute of Management Computer Engineering Department MH Pune India Computer Engineering GES Sapat COE MH Nashik India

ISBN: (纸本)9798350355611

This paper provides a comprehensive survey of Contextual AI applications for analyzing political discourse on Twitter, focusing on the latest advancements in natural language processing (NLP) and machine learning models. Analyzing Twitter data, with its informal language and rapid content generation, presents challenges like sarcasm, slang, and polarization. Contextual AI models, such as BERT and GPT-based architectures, offer significant improvements in political sentiment detection, emotion analysis, and bias identification. Key studies surveyed in this paper report high performance: the PoliBERT model achieved an F1 score of 0.95 and a precision of 0.96, while the sentiment analysis of political opinions showed balanced metrics with an F1 score of 0.89 and an accuracy of 0.88. A study on detecting political leanings achieved a 99% confidence level with an accuracy of 0.96 and ROC AUC of 0.95. These results demonstrate the potential of Contextual AI for accurately classifying political biases, real-time sentiment analysis, and handling complex language nuances. Future research should focus on real-time multilingual analysis, ethical considerations, and bias mitigation. This survey highlights the importance of Contextual AI in providing more precise, nuanced, and inclusive insights into political discourse on social media. © 2025 IEEE.

关键词： Tweets

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Medical Prescription Generator using natural language processing 3

Medical Prescription Generator using Natural Language Proces...

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

作者： Paithankar, Siddhesh Patil, Snehal Shivgan, Ashlesha Mahabudhe, Mandar Dharmadhikari, Pramod MIT Academy of Engineering School of Computer Engineering Pune India MIT Academy of Engineering School of Electrical Engineering Pune India

ISBN: (纸本)9798331523893

With the increasing population in India and the limited availability of medical professionals, time efficiency in diagnosis and prescription generation has become crucial. This paper proposes an automated system leveraging natural language processing (NLP) and natural language Generation (NLG) to assist doctors in generating patient history and prescription suggestions. The system utilizes previously typed words to predict the next word, drawing on a medical notes dataset tailored to the doctor's specialization. By giving keywords or short descriptions about a patient's condition, the system processes the input using NLP for contextual under-standing and employs NLG to generate detailed histories and prescriptions in a standardized format. This dual approach ensures that the output aligns with medical conventions while maintaining accuracy and efficiency. The system is trained on extensive medical datasets, optimizing its predictions over time through machine learning techniques. Our solution not only enhances accuracy but also reduces the time doctors spend on manual documentation, improving overall efficiency and patient care. © 2025 IEEE.

关键词： Diagnosis

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MusicBO, an Application of Text2AMR2FRED to the Musical Heritage Domain

MusicBO, an Application of Text2AMR2FRED to the Musical Heri...

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21st international conference on The Semantic Web (ESWC)

作者： Gangemi, Aldo Graciotti, Arianna Meloni, Antonello Marzi, Eleonora Nuzzolese, Andrea Presutti, Valentina Recupero, Diego Reforgiato Russo, Alessandro Tripodi, Rocco Univ Bologna I-40126 Bologna Italy Univ Cagliari Via Osped 72 I-09124 Cagliari Italy CNR Via San Martino Battaglia 44 I-00185 Rome Italy Ca Foscari Univ Venice Sestiere Dorsoduro 3246 I-30123 Venice VE Italy

ISBN: (纸本)9783031789519;9783031789526

Converting textual data into knowledge Graphs (KGs) poses a significant challenge, particularly when dealing with multilingual and historical documents. In this paper, we describe the application of Text2AMR2FRED to MusicBO corpus, the former being a tool for transforming text into RDF/OWL KGs via Abstract Meaning Representation (AMR), the latter being a diachronic collection of Musical Heritage (MH) texts.

关键词： Abstract Meaning Representation natural language processing knowledge Graphs Semantic Frames

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Applying natural language processing for Detecting Cybersecurity Threats Using Sentimental Analysis Techniques 5th

Applying Natural Language Processing for Detecting Cybersecu...

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

作者： Mishra, Awakash Ganesh, D. Sharma, Apurva Vignesh, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the ability to use herbal language processing (NLP) and Sentiment evaluation strategies to detect cybersecurity threats. We describe the traditional tactics and present-day tendencies in cybersecurity chance detection and discuss the demanding situations the technique confronts. We then present how NLP and Sentiment analysis can resource inside the detection technique, provide examples of ways NLP tools can assist in chance detection, and discuss how NLP can offer beneficial data in phrases of danger detection. We quickly review the strategies for classifying and verifying sentiment from textual statistics. Our findings propose that NLP and mawkish analysis can successfully discover cybersecurity threats and provide treasured insights for additional studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Integrated AI-Based Pipeline for Law Enforcement Application: FIR Assistance 5th

Integrated AI-Based Pipeline for Law Enforcement Application...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Amruth, A. Ramanan, R. Paul, Rhea Pranav, R. Gupta, Deepa Vekkot, Susmitha Nair, Priyanka C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819747108

This paper presents an integrated AI-based pipeline for law enforcement applications, specifically targeting the creation of first information report (FIR) in the Kannada language. The pipeline comprises a speech processing block utilizing automatic speech recognition (ASR) models to convert spoken Kannada into text, and a natural language processing block employing advanced AI techniques for information extraction and generating concise FIR summaries. The research focuses on optimizing and streamlining the FIR creation process by leveraging AI technologies and overcoming data scarcity in the Kannada language through data augmentation techniques. OpenAI’s Whisper is used for ASR, resulting in two-word error rate in this use case. For information extraction, a Bi-LSTM neural network is employed for named entity recognition (NER) with a performance of 94%. The proposed NER model utilizes three tags, which can be expanded to accommodate additional tags as per FIR requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： FIR filters

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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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Distill To Detect: Amplifying Anomalies in Backdoor Models through knowledge Distillation

Distill To Detect: Amplifying Anomalies in Backdoor Models t...

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

作者： Hu, Chang Teng, Xuyang Xing, Wenpeng Chen, Han Ye, Chenhao Han, Meng School of Communication Engineering Hangzhou Dianzi University Hangzhou China Binjiang Institute of Zhejiang University Zhejiang University Hangzhou China GenTel.io Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Backdoor attacks represent a significant threat to the security of deep learning models. Due to the stealthiness of backdoor attacks, effectively detecting whether a model has been compromised by such attacks remains a major challenge. Previous backdoor detection methods either rely on backdoor datasets to identify anomalies in backdoor samples or depend on prior knowledge of existing backdoor attacks. This results in difficulties in detecting backdoors when backdoor samples are unavailable and leads to poor generalization capabilities when addressing new attack methods. This work proposes a novel approach for detecting backdoor attacks called Distill To Detect (D2D), that does not depend on backdoor samples or any prior knowledge. It utilizes knowledge distillation to amplify more general and universal backdoor anomalies exhibited on clean samples for detection. This approach is not only more efficient but also exhibits strong generalization capabilities, enabling the detection of most backdoor attacks with low time and computational costs. We tested our approach against six backdoor attacks and three different model architectures, demonstrating the effectiveness of our proposed method. © 2025 IEEE.

关键词： AI security backdoor attack backdoor defense deep learning knowledge distillation

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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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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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BiLD: Bi-directional Logits Difference Loss for Large language Model Distillation 31

BiLD: Bi-directional Logits Difference Loss for Large Langua...

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31st international conference on Computational Linguistics, COLING 2025

作者： Li, Minchong Zhou, Feng Song, Xiaohui KTH Royal Institute of Technology Stockholm Sweden OPPO AI Center Beijing China

ISBN: (纸本)9798891761964

In recent years, large language models (LLMs) have shown exceptional capabilities across various natural language processing (NLP) tasks. However, such impressive performance often comes at the cost of an increased parameter size, posing significant challenges for widespread deployment. To address this issue, knowledge distillation (KD) provides a solution by transferring knowledge from a large teacher model to a smaller student model. In this paper, we explore the task-specific distillation of LLMs at the logit level. Our investigation reveals that the logits of fine-tuned LLMs exhibit a more pronounced long-tail distribution compared to those of vision models, with hidden "noise" in the long tail affecting distillation performance. Furthermore, existing logits distillation methods often struggle to effectively utilize the internal ranking information from the logits. To address these, we propose the Bi-directional Logits Difference (BiLD) loss. The BiLD loss filters out the long-tail noise by utilizing top-k teacher and student logits, and leverages the internal logits ranking information by constructing logits differences. To evaluate BiLD loss, we perform comprehensive experiments on 13 datasets with two types of LLMs. Our results show that the BiLD loss, with only the top-8 logits, outperforms supervised fine-tuning (SFT), vanilla KL loss, and five other distillation methods from both NLP and CV fields. Code is available at https://***/fpcsong/BiLD. © 2025 Association for Computational Linguistics.

关键词： Directional logging

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