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A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

A Decision Support System to Compile Environmental Mitigatio...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Yoon, Hong-Jun Ruggles, Tom Zhao, Huanhuan Singh, Debjani Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak RidgeTN37830 United States Oak Ridge National Laboratory Environmental Sciences Division Oak RidgeTN37830 United States University of Tennessee Knoxville Data Science and Engineering KnoxvilleTN37996 United States

ISBN: (纸本)9798350362480

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and ***'re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the *** system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States. © 2024 IEEE.

关键词： natural language processing systems

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A Transformer-Based Network for Hyperspectral Image Classification 1

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11th international conference on Communications, Signal processing, and Systems, CSPS 2022

作者： Yu, Jizhen Li, Zhengtao School of Electronic and Communication Engineering Tianjin Normal University Tianjin300387 China

ISBN: (数字)9789819923625

ISBN: (纸本)9789819923618

For hyperspectral classification, many pixel-based approaches have been developed to deal with multidimensional hyperspectral data, in addition to the orderless vector-based feature extraction, the sequence-based feature representation approaches also prove its effect on hyper-spectral classification. Based on the sequence-based data structure, we borrow the Transformer architecture from the knowledge of NLP (natural language processing) to analyze hyperspectral pixels as sequential data and then decide information categories via network reasoning. In the proposed model, after first obtaining the spatial features of single band, we can obtain the spectral embeddings and position embeddings in hyperspectral sequence;Secondly, with adopting multilayer encoder module derived from Transformer which has powerful sequence expression ability, we can dig depth correlation between sequence data;Finally, a feedforward layer and a softmax layer are used to predict class categories. The experimental results on two publicly accessible images demonstrate that our proposed approach can obtain competitive performance compared with other state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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BERT Model Compression with Decoupled knowledge Distillation and Representation Learning 4

BERT Model Compression with Decoupled Knowledge Distillation...

引用

4th international conference on Advanced Information Science and System, AISS 2022

作者： Zhang, Linna Chen, Yuehui Cao, Yi Zhao, Yaou School of Information Science and Engineering University of Jinan China

ISBN: (纸本)9781450397933

Pre-Trained language models such as BERT have proven essential in natural language processing(NLP). However, their huge number of parameters and training cost make them very limited in practical deployment. To overcome BERT's lack of computing resources, we propose a BERT compression method by applying decoupled knowledge distillation and representation learning, compressing the large model(teacher) into a lightweight network(student). Decoupled knowledge distillation divides the classical distillation loss into target related knowledge distillation(TRKD) and non-Target related knowledge distillation(NRKD). Representation learning pools the Transformer output of each two layers, and the student network learns the intermediate features of the teacher network. It has better results on tasks of Sentiment Classification and Paraphrase Similarity Matching, retaining 98.9% performance of the large model. © 2022 ACM.

关键词： Distillation

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Construction method of Geographic knowledge Graph based on Micro-Annotation

Construction method of Geographic Knowledge Graph based on M...

引用

Big Data & Artificial Intelligence & Software engineering (ICBASE), international conference on

作者： Xingshu Wang Ying Nie North China Institute of Computing Technology NCI Beijing China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

This paper discusses the construction method of geographic knowledge map based on the combination of geographic information system (GIS) and artificial intelligence. In this paper, a construction method based on micro-annotation is proposed. By optimizing the process of data acquisition and annotation, representative texts are selected for annotation, and iterative training is performed to improve the efficiency and accuracy of entity relation extraction. A structured data collection framework is designed, natural language processing technology is used to automatically extract key information from geographic text, and the geographic knowledge map is constructed for experimental verification. The results show that the proposed method is superior to traditional methods in terms of construction cost, training speed and extraction accuracy. At the end of the paper, the limitation and future direction of the research are discussed, which provides a reference for the further study of geographical knowledge map.

关键词： Training Accuracy Annotations knowledge graphs Data models natural language processing Data mining Reliability Geographic information systems Software engineering

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VSCODE- Code With Voice Using natural language processing(NLP) 1

VSCODE- Code With Voice Using Natural Language Processing(NL...

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1st international conference on Computer, Power and Communications, ICCPC 2022

作者： Prakash, M. Gnana Ponmalar, A. Deeba, S. Akilandeswari, A. Rasool, Saira Banu Mohammed Lavanya, P. Sri Sai Ram Institute of Technology Department of Information Technology Tamil Nadu Chennai India Sri Manakula Vinayagar Engineering College Department of Computer Science and Engineering Tamil Nadu Puducherry India Saveetha School of Engineering Simats Tamil Nadu Chennai India Applied College Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350397840

Physically Challenged people who are interested in coding, in the current scenario cannot code and exhibit their knowledge in programming skills. Our project enables them to code without using a keyboard. They can code entirely by their voice and execute their code. We are converting the user's speech to text by natural language processing (NLP). With the use of text obtained from NLP model we generate code snippets using our custom code generator API. The code snippets are generated in the Visual Studio Code text editor using our own VSCode extension. © 2022 IEEE.

关键词： Speech recognition

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

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

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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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Neural Networks in Recruitment: Trends and Future Directions 1

Neural Networks in Recruitment: Trends and Future Directions

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1st IEEE international conference on Ambient Intelligence, knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Khurana, Rahul Yadav, Mohit Quttainah, Majdi Srivastava, Anugamini Priya Baludi, Arun Singh, Priyank Kumar Indian Institute of Technology Department of Management Studies Roorkee India Jindal Global Business School Op Jindal Global University Sonipat India College of Business Administration Kuwait University Kuwait Symbiosis Institute of Business Management Symbiosis International Deemed University Pune India Dayananda Sagar University Department of Electronics and Communication Engineering Bengaluru India School of Management Doon University Dehradun India

ISBN: (纸本)9798350316469

A thorough bibliometric analysis of 464 academic publications published between 1985 and 2023 is presented in this study to analyse current trends and potential future directions for neural networks in recruiting. It looks at publishing patterns, well-known publications, and notable contributors to this area-authors, institutions, and nations. The findings help researchers choose credible journals for publishing by offering insightful information on major research themes and illuminating developing areas within this domain. Researchers need to investigate the use of neural networks in new recruiting fields, such as prejudice reduction, diversity and inclusion, ethical considerations, and privacy issues. Future research might focus on using neural networks in group decision-making procedures and the fusion of neural networks with other cutting-edge technologies like machine learning and natural language processing. © 2023 IEEE.

关键词： bibliometric analysis neural networks recruitment talent acquisition

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PLP 2021: Workshop on Programming language processing 21

PLP 2021: Workshop on Programming Language Processing

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27th ACM SIGKDD international conference on knowledge Discovery and Data Mining (KDD)

作者： Xu, Chang Ma, Siqi Lo, David Univ Sydney Sydney NSW Australia Univ Queensland Brisbane Qld Australia Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450383325

The first international Workshop on Programming language processing presents interdisciplinary contributions that address programming language procession problems with machine learning and data mining techniques. Recently, there are lots of successful natural language processing methods. But the mining of programming languages could not exactly follow the manner of natural language processing. The difference between natural language and programming language brings in new research challenges and opportunities. The workshop will bring together researchers from machine learning, data mining and software engineering to discuss and debate the path forward for mining the value of programming languages.

关键词： Programming language natural language processing machine learning software engineering data mining

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Recent Trends for Text Summarization in Scientific Documents 9

Recent Trends for Text Summarization in Scientific Documents

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9th IEEE international conference on Computing, engineering and Design, ICCED 2023

作者： Sukmandhani, Arief Agus Arifin, Yulyani Zarlis, Muhammad Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required;therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization. © 2023 IEEE.

关键词： Text processing

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Automated Sinhala Voice Assistant to Manage Tasks using natural language processing-‍ Voice 3

Automated Sinhala Voice Assistant to Manage Tasks using Natu...

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3rd international Informatics and Software engineering conference, IISEC 2022

作者： Senarathne, K.H.I.R. Nirash, J.M.I. Herath, H.M.C.P. Bandara, V.D. Wijendra, Dinuka Krishara, Jenny Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Voice assistants are programs on digital devices that listen and respond to verbal commands. In this dynamic world, users can use these voice assistants to manage daily tasks, plan their day, get answers to problems, and for entertainment purpose. Most of existing voice assistant applications functioned using the English language. Since Sinhala is the native language in Sri Lanka, it is not recognized internationally as well as within Sri Lanka for technical applications. As Sri Lankans are more inclined to use the Sinhala language, it is expected to develop this for the benefit of all the Sri Lankans despite their age and to use their native language through a technical application. Furthermore, the lack of English knowledge will lead to the decline of Information technology literacy. This project expects to take the Sinhala language forward to a standard where it is recognized locally and internationally. Thus, building a mobile application that supports Sinhala voice commands will solve the above-mentioned issues. It will facilitate the individuals to do daily activities efficiently and effortlessly within a less time. Machine Learning and natural language processing are the man technologies used in this project. A computer vision-based algorithm from face detection technology is trained. This application is capable of answering questions and following the instructions for daily tasks and also can be used for entertainment. Apart from the mentioned specialty, it also supports and responds to the Sinhala language that will be shown off on a display. © 2022 IEEE.

关键词： Face recognition

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