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Design and implementation of intelligent search assistant based on AIGC and RPA 24

Design and implementation of intelligent search assistant ba...

引用

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

作者： Lin, Meirong Zheng, Zhiguo Information Engineering College Hainan Vocational University of Science and Technology Hainan Haikou China School of information and communication engineering Hainan University Hainan Haikou China

ISBN: (纸本)9798400710094

With the rapid development of artificial intelligence technology, intelligent search assistant has become a key tool to improve the efficiency of information retrieval. This paper presents a design and implementation scheme of intelligent search assistant based on artificial intelligence-generated Content (AIGC) and robotic process automation (RPA). By integrating natural language processing (NLP) technology, the system realizes accurate understanding and intelligent analysis of user queries, and uses RPA technology to automate search tasks, which can quickly understand user query intentions and provide accurate search results. The experimental results show that the intelligent search assistant is superior to the traditional search tools in terms of information retrieval speed and accuracy, and provides users with an efficient means of information acquisition in the era of information explosion. It provides an efficient means of information acquisition for users in the era of information explosion. © 2024 Copyright held by the owner/author(s).

关键词： Structured Query language

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A Unified Framework for Efficient Task-Oriented Dialogue System Using Prompts 6th

A Unified Framework for Efficient Task-Oriented Dialogue Sys...

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6th international conference on communication and computational technologies, ICCCT 2024

作者： Thakkar, Manisha Pise, Nitin N. Department of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University Pune India

ISBN: (纸本)9789819774258

Task-oriented dialogue (TOD) systems significantly impact our daily lives, occupying a pivotal role within the realm of natural language processing (NLP). Pretrained language models (PLMs) have revolutionized these systems with enhanced capabilities and notable performance improvements. Conventionally, PLMs require extensive pretraining followed by specialized fine-tuning for each downstream task. This approach faces significant hurdles in practice due to the requirement of large computing power to store and update the model parameters and the scarcity of task-specific annotated data to achieve desired task performance. We propose a unified framework that utilizes task-specific prompts to address these limitations. The prompting technique unleashes the power of the shared PLM to scale effectively with fewer demonstrations or task-specific descriptions. Our experiments demonstrate that the proposed approach adapts to diverse tasks across domains with significant performance in a computationally efficient manner compared to the traditional paradigm of fine-tuning PLMs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： natural language processing systems

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

引用

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

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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Medicinal Plant Identification and Information Provision Using AI

Medicinal Plant Identification and Information Provision Usi...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Anslam Sibi, S. Sundaresan, C. Surya, S. Sujith, J.P. Srm Valliammai Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Medicinal plants have been integral to traditional medicine, offering a wide range of health benefits and natural remedies. However, accurate identification is essential for safe use and the preservation of this valuable knowledge. This project proposes an AI-based system that combines EfficientNet and XGBoost models to enhance the accuracy of medicinal plant identification from images. While traditional systems rely on manual identification or basic models like CNN and SVM, they often fail to account for variations in plant appearance due to growth stages or environmental conditions. To address these limitations, the proposed system leverages EfficientNet for robust feature extraction from plant images, utilizing its superior ability to handle image variations across diverse conditions. These features are then passed to an XGBoost classifier, which excels in handling structured data and fine-tuning predictions. Together, this combination ensures high identification accuracy across a wide variety of plant species. Additionally, the system integrates a Hugging Face Transformer for natural language understanding and generation, allowing it to provide comprehensive information about each plant's medicinal properties and uses. This approach bridges the gap between accurate identification and practical application, promoting safe usage and preserving traditional medicinal knowledge. © 2025 IEEE.

关键词： Medicinal chemistry

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

作者： Ghosh, Tiash Rasheed, Razeen A. Singh, Sanjai Kumar Jenamani, Mamata Routray, Aurobinda Dept. of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India GEOPIC Oil & Natural Gas Corporation Dehradun India Dept. of Industrial & Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9798350368741

Seismic Fault Detection is a crucial aspect of oil exploration. While traditional deep learning methods struggle to handle complex seismic data patterns, training a deep learning model solely on synthetic seismic data may not yield satisfactory results. This research paper, involves utilizing a pre-trained vision-based transformer to extract relevant features from seismic data. By leveraging the knowledge learned from a different but related task, the model can capture general fault patterns in field data. In this framework, a portion of the pre-trained model architecture is employed and trained using a Structural Similarity-based loss function to learn fault-related features. This allows the model to adapt to the faulted structures of a geological dataset and improve fault detection performance in field data applications. In comparison to the state-of-the-art method, the proposed method yields improved results on real field dataset. © 2025 IEEE.

关键词： knowledge transfer Seismic fault detection Structural similarity loss Transformer architecture

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Improving Robotic Arms Through natural language processing, Computer Vision, and Edge Computing 3

Improving Robotic Arms Through Natural Language Processing, ...

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3rd international conference on Mechatronics, Control and Robotics, ICMCR 2025

作者： Sikorski, Pascal Yu, Kaleb Billadeau, Lucy Esposito, Flavio Aliakbarpour, Hadi Babaias, Madi Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Computer Science Department Saint Louis United States Saint Louis University Aerospace & Mechanical Engineering Department Saint Louis United States

ISBN: (纸本)9798331544546

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with natural language processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. © 2025 IEEE.

关键词： Robotic arms

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Estimation of Offensiveness of Posts on Social Media and Its Application to a Conversation Assistance System 24

Estimation of Offensiveness of Posts on Social Media and Its...

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

作者： Fujihara, Tomoki Ito, Akinori Nose, Takashi Graduate School of Engineering Tohoku University Miyagi Sendai Japan

ISBN: (纸本)9798400717383

This study aims to promote smooth communication on social media by automatically responding to posts that may or may not be perceived as offensive depending on the reader and context (gray zone posts). To achieve this, we propose a conversation support method that automatically detects gray zone posts and provides additional information about the post to both the reader and the writer. In this paper, we build an offensiveness estimation model to detect gray zone posts automatically. The model was implemented the language model LUKE, which is a similar LM to BERT. Furthermore, we verify whether the proposed method can reduce the offensiveness perceived by the reader. The support posts are generated by ChatGPT based on the emotion classification result. As a result, the proposed method did not reduce the perceived offensiveness from the entire posts. The offensiveness estimation model built in this experiment is publicly available at https://***/TomokiFujihara. © 2024 Copyright held by the owner/author(s).

关键词： Social media

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Earth Observation Data Management: A knowledge Graph-Based Approach

Earth Observation Data Management: A Knowledge Graph-Based A...

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2024 international KES conference on Human Centred Intelligent Systems

作者： Domalis, Georgios Giarelis, Nikolaos Gioutlakis, Aris Karacapilidis, Nikos Livieris, Ioannis E. Tsakalidis, Dimitris Novelcore Athens 10436 Greece Univ Patras IMIS Lab Patras 26504 Greece Univ Piraeus Dept Stat & Insurance Sci Piraeus 18532 Greece

ISBN: (纸本)9789819786008;9789819785988;9789819785971

From weather forecasts to civil protection applications, earth observation data provide valuable input for policy-making towards a more sustainable society. During the last decade, several platforms and applications have been developed to store, process and analyse big and heterogeneous volumes of such data. Having thoroughly considered various techniques and approaches used to index, analyse and reason about these types of data, this paper proposes a hybrid knowledge graph and semantic-based approach for querying earth observation data sources. The proposed approach acts as a unified framework for diverse earth observation data sources, which does not only enhance data accessibility, but also enables the discovery of previously hidden knowledge and the extraction of meaningful insights.

关键词： Earth observation data knowledge graph natural language processing Semantic annotation

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Edit-Wise Preference Optimization for Grammatical Error Correction 31

Edit-Wise Preference Optimization for Grammatical Error Corr...

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31st international conference on Computational Linguistics, COLING 2025

作者： Liang, Jiehao Yang, Haihui Gao, Shiping Quan, Xiaojun School of Computer Science and Engineering Sun Yat-sen University China

ISBN: (纸本)9798891761964

While large language models (LLMs) have achieved remarkable success in various natural language processing tasks, their strengths have yet to be fully demonstrated in grammatical error correction (GEC). This is partly due to the misalignment between their pre-training objectives and the GEC principle of making minimal edits. In this work, we aim to bridge this gap by introducing a novel method called Edit-wise Preference Optimization (EPO). By distinguishing the importance of different tokens and assigning higher reward weights to edit tokens during preference optimization, our method captures fine-grained distinctions in GEC that traditional preference learning often overlooks. Extensive experiments on both English and Chinese datasets show that our framework consistently outperforms strong baselines, achieving state-of-the-art performance and demonstrating the advantages of LLMs in GEC. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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The Use of Social Media in Generating Health Information Support for Spanish Users 16

The Use of Social Media in Generating Health Information Sup...

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16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Elizarraras, Omar Chakraborty, Joyram Towson University 8000 York Rd Towson Maryland21204 United States

The popularity of online social networks (OSNs) has transformed the way individuals seek and share knowledge related to healthcare. By utilizing the informative power of OSNs, healthcare systems themselves have been able to share relevant information with the public. Especially during the COVID-19 pandemic, healthcare systems and government organizations leveraged OSN to share health recommendations and illness trends. OSNs proved their value in allowing organizations and the public to share information related to throughout their online groups. Of note, Reddit is a prominent OSN that provides users a platform to discuss a plethora of topics and have in-depth discussions, to include healthcare related conversations. This aspect of OSNs is the true value as it allows researchers and organizations the ability to identify users' sentiment trends, topic modeling, and perceptions of the healthcare systems. This study proposes developing a health-focused application designed to leverage the vast amount of user-generated content related to Mexican and Argentinian healthcare systems. The proposed application allows users to ask questions and receive responses derived from healthcare-related Reddit comments most closely related to their queries, and allows healthcare professions to identify gaps in services and public misperceptions of their healthcare system and services. Core functionality of the application is powered by natural language processing (NLP) algorithms, such as text-To-Text transfer transformer (T5) and Bidirectional Encoder Representations from Transformers (BERT) both developed by Google. The backend model will use extractive functionality to match user generated question to one generated using T5 model. Based on the question match, the application will retrieve a Reddit comment using a pre-Trained BERT model specifically trained on Spanish datasets called BETO, and Facebook AI similarity search (FAISS). This approach provides users with diverse perspective

关键词： natural language processing systems

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