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LearnLytics: A world of knowledge at your fingertips 5

LearnLytics: A world of knowledge at your fingertips

引用

5th international conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Koul, Nimrita Valluru, Vikas Sundarrajan, Sanvi Menon, Adarsh Mathur, Apoorv Reva University Department of Computer Science Bangalore India

ISBN: (纸本)9798331522100

LearnLytics is a cutting-edge educational web platform designed to revolutionize the way students and educators engage in the learning *** platform, which emphasizes individualized learning, providing a variety of courses, materials, and interactive technologies that foster academic development , the app encourages cooperation between teachers and students. A dynamic learning environment that adjusts to each learner's needs is provided by LearnLytics.A chatbot for question-answer evaluation, video lessons, study resources, and teacher-student interaction modules provide comprehensive academic *** chatbot was engineered using the Gemini Flash model, followed by a fine-tuning process to improve its suitability for academic *** educational process is simplified for instructors, while the platform endeavors to foster a resilient, never-give-up attitude in *** is positioned as a revolutionary tool that will influence education in the future thanks to these creative solutions. © 2025 IEEE.

关键词： natural language processing systems

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Group-CLIP Uncertainty Modeling for Group Re-Identification

Group-CLIP Uncertainty Modeling for Group Re-Identification

引用

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

作者： Zhang, Qingxin Wei, Haoyan Qian, Yang Glasgow College Hainan UESTC University of Electronic Science and Technology of China Hainan China School of Electronic Information Sichuan University Chengdu China USC Viterbi School of Engineering University of Southern California Los AngelesCA United States

ISBN: (纸本)9798350368741

Group Re-Identification (Group ReID) aims matching groups of pedestrians across non-overlapping cameras. Unlike single person ReID, Group ReID focuses more on the changes in group structure, emphasizing the number of members and their spatial arrangement. However, most methods rely on certainty-based models, which consider only the specific group structures in the group images, often failing to match unseen group configurations. To this end, we propose a novel Group-CLIP Uncertainty Modeling (GCUM) approach that adapts group text descriptions to undetermined accommodate member and layout variations. Specifically, we design a Member Variant Simulation (MVS) module that simulates member exclusions using a Bernoulli distribution and a Group Layout Adaptation (GLA) module that generates uncertain group text descriptions with identity-specific tokens. In addition, we design a Group Relationship Construction Encoder (GRCE) that uses group features to refine individual features, and employ cross-modal contrastive loss to obtain generalizable knowledge from group text descriptions. It is worth noting that we are the first to employ CLIP to Group ReID, and extensive experiments show that GCUM significantly outperforms state-of-the-art Group ReID methods. © 2025 IEEE.

关键词： CLIP Group Re-Identification Uncertainty Modeling Vision-language Pre-training

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Sentiment Impact on Fake News Detection: A Preliminary Study 20th

Sentiment Impact on Fake News Detection: A Preliminary Study

引用

20th international conference on Information processing and Management of Uncertainty in knowledge-Based Systems

作者： Lomasto, Luigi Aurucci, Raffaele Brandi, Yuri Gajewski, Lukasz Lettieri, Nicola Malandrino, Delfina Zaccagnino, Rocco Univ Salerno Dept Comp Sci Fisciano Italy Natl Inst Publ Policy Anal INAPP Rome Italy

ISBN: (纸本)9783031739965;9783031739972

The information disorder phenomenon represents one of the main challenges for the current society, that researchers of a huge variety of scientific areas are trying to solve. To date, the majority of the studies carried out in this context are focused on Machine Learning-based Fake News detectors trained on textual data. Despite the numerous attempts available in the literature and the promising results of such models, unfortunately, the expectations are not truly met considering their use in real-world scenarios. The main limitations are directly related to the nature of the phenomenon since the model trained on past events can't understand and classify novel contents and breaking news. In the current work, we study if sentiment discrepancy between news and its associated evidence can determine the possible presence of fake content. In fact checking activities, the evidence are inferred claims or information used to accept or reject shared news [2]. To do that we have conducted a preliminary study in which the outcome is a metric called Negativity Score. This metric can be used as a feature in Fake News detection models and automated fact-checking activities. For completeness, we have also provided a framework that uses the proposed metric. The results of preliminary experiments highlight the possibility of exploiting sentiment aspects in addition to more common and well-known approaches.

关键词： Information Disorder Fake News Automated Fact-Checking Sentiment Analysis natural language processing

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Online Learning from Strategic Human Feedback in LLM Fine-Tuning

Online Learning from Strategic Human Feedback in LLM Fine-Tu...

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

作者： Hao, Shugang Duan, Lingjie Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350368741

Reinforcement learning from human feedback (RLHF) has become an essential step in fine-tuning large language models (LLMs) to align them with human preferences. However, human labelers are selfish and have diverse preferences. They may strategically misreport their online feedback to influence the system's aggregation towards their own preferences. Current practice simply averages labelers' feedback per time and fails to identify the most accurate human labeler, leading to linear regret O(T) for T time slots. To our best knowledge, we are the first to study online learning mechanisms against strategic human labelers in the LLM fine-tuning process. We formulate a new dynamic Bayesian game and dynamically adjust human labelers' weights in the preference aggregation, ensuring their truthful feedback and sublinear regret O(T1/2). Simulation results demonstrate our mechanism's great advantages over the existing benchmark schemes. © 2025 IEEE.

关键词： LLM fine-tuning online learning regret analysis strategic human feedback truthful mechanism design

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The Applications and Research of NLP and Deep Learning Technologies in Public Security Data 24

The Applications and Research of NLP and Deep Learning Techn...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Zhu, Yupeng Zhu, Yuhao College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China The Smart City Research Institute of China Electronics Technology Group Corporation Guangdong Shenzhen China

ISBN: (纸本)9798400710094

With the deepening reform and construction of the police system, it is imperative to fully utilize technologies such as artificial intelligence, big data, blockchain, cloud computing, and the Internet of Things to rapidly enhance the intelligence level of the new generation command center. natural language processing and deep learning technologies can achieve recognition, understanding, and correlation analysis of public security textual information, which are core steps in building intelligence. Due to the diversity of information in the public security field, the accuracy of these technologies still requires further research. In the analysis of public security incidents, the application of NLP technology faces challenges in optimizing technical models and algorithms, which greatly enhances the efficiency and accuracy of public safety management by analyzing, understanding, predicting, and responding to various security challenges in an intelligent manner. With the advent of the big data era, the public security sector has accumulated massive amounts of police incident data, which contain rich information but also bring difficulties in processing and analysis. Traditional NLP models often struggle to cope with the complex and ever-changing police incident texts, leading to compromised accuracy and reliability of the analysis results. This paper will explore the key technological applications of NLP and deep learning in the field of public safety and provide a review of their research progress. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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A Domain-Specific Multilingual Speech Translation Corpus via Simultaneous Interpretation

A Domain-Specific Multilingual Speech Translation Corpus via...

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

作者： Han, Seunghee Lee, Gary Geunbae Kim, Hyung Soon Kim, Sunhee Chung, Minhwa Learning Sciences Research Institute Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Postech Pohang Korea Republic of Department of Electronics Engineering Pusan National University Busan Korea Republic of Department of French Language Education Seoul National University Seoul Korea Republic of Department of Linguistics Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798350368741

This paper presents a novel multilingual speech translation corpus for complex, domain-specific content in Korean, English, Spanish, and Japanese. The corpus contains 4, 000 hours of parallel speech, including 1, 000 hours of Korean audio with simultaneous sight interpretations in the other three languages by 294 professionals (242 interpreters and 52 Korean voice actors). It also includes transcriptions, translations, and annotations for all languages. The Dewey Decimal Classification was adapted to balance knowledge representation, and speech tasks were conducted in a controlled studio environment to ensure data consistency. Translation, transcription, and annotation workflows were managed through a custom-built platform. The corpus captures nuanced contexts, cultural sensitivities, and domain-specific terminology, addressing linguistic challenges like structural differences between SOV (Korean, Japanese) and SVO languages (English, Spanish). Preliminary evaluations indicate its potential to enhance end-to-end speech translation models, support cross-lingual transfer learning, and tackle real-time translation issues. © 2025 IEEE.

关键词： Speech processing resources Speech recognition Speech synthesis Speech-to-speech translation

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SSE: A Speaking Style Extractor Based on Fine-Grained Contrastive Learning between Speech and Descriptive Text

SSE: A Speaking Style Extractor Based on Fine-Grained Contra...

引用

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

作者： Zhang, Zixing Wu, Yimeng Dong, Zhongren Xiang, Wulong Shen, Shengfan Schuller, Björn W. College of Computer Science and Electronic Engineering Hunan University China Shenzhen Research Institute Hunan University China GLAM Department of Computing Imperial College London United Kingdom

ISBN: (纸本)9798350368741

Effective extraction of paralinguistic features from speech, such as emotion, accent, and age, remains a challenging task in speech processing. Traditional methods typically address each type of paralinguistic information with separate classification or regression tasks-e. g., emotion recognition, accent detection, and age estimation. These approaches can result in fragmented and incomplete descriptions of speech style, failing to capture the full range of paralinguistic attributes in an integrated way. To address these limitations, we propose the Speech Style Extractor (SSE), a novel approach that aims to provide a more comprehensive extraction of speech style features. SSE leverages an optimized fine-grained contrastive learning scheme, enhancing the extraction of diverse paralinguistic features. The optimization approach outperforms the baseline on 11 out of 13 datasets, with an average improvement of 2.1% in terms of the R@1 evaluation metric. We also propose a complete data generation framework using large language models to create 199k text samples across 9 paralinguistic categories, such as emotion, deception, stuttering, and accent, to fill the gap in speaking style descriptions in public datasets. This extensive dataset not only facilitates our research but also serves as a valuable resource for the speech processing community. © 2025 IEEE.

关键词： Contrastive Learning Fine-Grained Loss Prompt engineering Speaking Styles

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iText2KG: Incremental knowledge Graphs Construction Using Large language Models 25th

iText2KG: Incremental Knowledge Graphs Construction Using La...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Lairgi, Yassir Moncla, Ludovic Cazabet, Rémy Benabdeslem, Khalid Cléau, Pierre INSA Lyon CNRS Universite Claude Bernard Lyon 1 LIRIS UMR5205 Villeurbanne69621 France GAUC Lyon France

ISBN: (纸本)9789819605729

Most available data is unstructured, making it challenging to access valuable information. Automatically building knowledge Graphs (KGs) is crucial for structuring data and making it accessible, allowing users to search for information effectively. KGs also facilitate insights, inference, and reasoning. Traditional NLP methods, such as named entity recognition and relation extraction, are key in information retrieval but face limitations, including predefined entity types and the need for supervised learning. Current research leverages large language models’ capabilities, such as zero- or few-shot learning. However, unresolved and semantically duplicated entities and relations still pose challenges, leading to inconsistent graphs and requiring extensive post-processing. Additionally, most approaches are topic-dependent. In this paper, we propose iText2KG (The code and the dataset are available at https://***/AuvaLab/itext2kg), a method for incremental, topic-independent KG construction without post-processing. This plug-and-play, zero-shot method is applicable across a wide range of KG construction scenarios and comprises four modules: Documents Distiller, Incremental Entities Extractor, Incremental Relations Extractor, and Graph Integrator. Our method demonstrates superior performance compared to baseline methods across three scenarios: converting scientific papers to graphs, websites to graphs, and CVs to graphs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： knowledge graph

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Conversational Chatbot with RASA for Educational Facilitation 6

Conversational Chatbot with RASA for Educational Facilitatio...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kale, Varun Rana, Khumarya Wade, Aayush Patel, Krish Sirsat, Ajay Departmant of Computer Science St John College of Engineering and Management Palghar India

ISBN: (纸本)9798331522667

The evolution of Artificial Intelligence (AI) has become integral in evolution of industrial landscape. Artificial Intelligence is used in healthcare, finance, manufacturing etc. We can use Artificial Intelligence tools for tailored use cases in different industries one of them being the education industry. In this research, we use RASA an open-source framework for conversational AI, focusing on potential for AI-driven career counseling chatbot. Using Rasa and machine learning algorithm particularly the Support Vector Machine (SVM), the research delivers a chatbot that delivers personalized academic advice. The chatbot will take user grade in core subjects like Mathematics, Science, English etc. and will provide recommendations and adapt according to the user’s choice. Rasa’s natural language Understanding (NLU) feature a branch of natural language processing (NLP) will play an important role in classifying user input and extract relevant information. This research studies the pros and cons of using open-source rasa framework to create educational chatbot highlighting the potential to transform traditional career counseling. © 2025 IEEE.

关键词： Support vector machines

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Real-Time Interaction in AR/VR Environments: A Deep Learning Approach to Human-Computer Interaction 1

Real-Time Interaction in AR/VR Environments: A Deep Learning...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chindiyababy, U. Mehta, Vishal Kakkar, Preetish Dilbar, Matchanova Umidbek, Abdalov Sharma, Aditya SRM Valliammai Engineering College Department of Information Technology India India Urgench state university Uzbekistan Mamun university Department of History Khiva Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331518578

AR and VR technologies represent Innovative technologies of HCI providing rich and dynamic user experiences. This work proposes a framework based on deep learning for interacting in real-time with AR/VR environments and employing innovative multimodal input handling and flexible response strategies. The implementation consists of convolutional neural networks (CNNs) for gesture recognition, recurrent neural models for natural language understanding, and transformer-based models for gaze tracking. These models are latency-reduced and are deployed with minimal overhead architectures to fit into constrained AR/VR devices. Several assessments are done based on actual data, and mock AR/VR is used to help prototypes. Experimental evaluation confirms that the proposed framework has better interaction latency, accuracy, and adaptability over conventional approaches. For instance, gesture recognition has a success rate of 96.3% the response is facilitated in less than 50 and the natural language commands have a processing delay of 30 ms. The user surveys support the case that the system effectively presents context-based and individualized communication making the system more engaging and easier to use. It focuses on the issues that arise while implementing such methods, including computational load and integration difficulties, and provides a more detailed version of this work that describes solutions to them. The findings map the feasibility of deep learning in augmenting HCI within the AR/VR systems, which will help propel advancements across education, healthcare, and more notably, game-designing and telepresence. © 2025 IEEE.

关键词： Convolutional neural networks

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