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Multi-source Open Data Quality Evaluation Model in the web 3.0 Era

Multi-source Open Data Quality Evaluation Model in the Web 3...

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Data Science and information technology (DSIT), international conference on

作者： Tianan Yan Zhaosen You Yifan Zhang Ruikai Hua Engineering Center Institute of Scientific and Technical Information of China Beijing China Schools of Software Engineering University of Science and Technology of China Suzhou China Institute of Statistics Xi’an University of Finance and Economics Xi’an China JIANGNAN RURAL COMMERCIAL BANK Changzhou China

In the web 3.0 era, information sources were distributed in different locations, and data collectors collected data with issues such as inconsistent data formats and missing data fields, resulting in the inability to conduct data analysis and provide accurate decision-making services. According to the actual process of multi-source open data collection, the evaluation process is divided into three parts: multi-source monitoring quality, original open data quality and processing open data quality. According to the open data quality evaluation indexes set up at different stages, the weight value of each evaluation index is calculated according to the scoring situation, and the open data quality evaluation model oriented to the characteristics of multi-source is constructed. It is found that the multi-source monitoring quality and the open data itself quality should be ensured first, and then is the quality of open data after data processing, in order to better improve the overall multi-source open data quality and provide support for data analysis and intelligence services.

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Evaluating Prompt-Based Question Answering for Object Prediction in the Open Research Knowledge Graph 34th

Evaluating Prompt-Based Question Answering for Object Predic...

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34th international conference on Database and Expert Systems Applications (DEXA)

作者： D'Souza, Jennifer Hrou, Moussab Auer, Soeren TIB Leibniz Informat Ctr Sci & Technol Hannover Germany Leibniz Univ Hannover Hannover Germany Leibniz Univ Hannover L3S Res Ctr Hannover Germany

ISBN: (纸本)9783031398469;9783031398476

Recent investigations have explored prompt-based training of transformer language models for new text genres in low-resource settings. this approach has proven effective in transferring pre-trained or fine-tuned models to resource-scarce environments. this work presents the first results on applying prompt-based training to transformers for scholarly knowledge graph object prediction. Methodologically, it stands out in two main ways: 1) it deviates from previous studies that propose entity and relation extraction pipelines, and 2) it tests the method in a significantly different domain, scholarly knowledge, evaluating linguistic, probabilistic, and factual generalizability of large-scale transformer models. Our findings demonstrate that: i) out-of-the-box transformer models underperform on the new scholarly domain, ii) prompt-based training improves performance by up to 40% in relaxed evaluation, and iii) tests of the models in a distinct domain reveals a gap in capturing domain knowledge, highlighting the need for increased attention and resources in the scholarly domain for transformer models.

关键词： Question Answering Prompt-based Question Answering Natural language processing Knowledge Graph Completion Open Research Knowledge Graph

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A Semantic Approach to Negation Detection and Word Disambiguation with Natural language processing 22

A Semantic Approach to Negation Detection and Word Disambigu...

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Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： Izunna Okpala Guillermo Romera Rodriguez Andrea Tapia Shane Halse Jess Kropczynski Information Technology University of Cincinnati USA Information Science and Technology Penn State University USA

ISBN: (纸本)9781450397629

this study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text via word-sense disambiguation. the proposed framework examines all the unique features in the various expressions within a text to resolve the contextual usage of all tokens and decipher the effect of negation on sentiment analysis. the application of popular expression detectors skips this important step, thereby neglecting the root words caught in the web of negation and making text classification difficult for machine learning and sentiment analysis. this study adopts the Natural language processing (NLP) approach to discover and antonimize words that were negated for better accuracy in text classification using a knowledge base provided by an NLP library called WordHoard. Early results show that our initial analysis improved on traditional sentiment analysis, which sometimes neglects negations or assigns an inverse polarity score. the SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20% and the TextBlob by 6%.

关键词： Artificial Intelligence Machine Learning Negation Detection Text analysis Word Sense Disambiguation

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Inflammatory language Recognition in Text Dependent Preset Set Attributes and the Bert Construction Process

Inflammatory Language Recognition in Text Dependent Preset S...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： V.A. Narayana K. Venkateswara Rao L Chandra Sekhar Reddy K Maheswari Jonnadula Narasimharao B. Karunasree Department of CSE CMR College of Engineering& technology Hyderabad Telangana India Department of CSE CMR Technical Campus Hyderabad Telangana India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

the utilization of social networking sites has witnessed a parallel growth in tan-dem with the increasing significance of the web as a means of both private and professional interaction. In many digital platforms such as online communities and Twitter, individuals have the ability to contribute remarks. the proliferation of derogatory and offensive comments has emerged as a notable concern alongside the growing acceptance of the web. It is plausible that certain persons utiliz-ing social networking platforms are actively spreading information that promotes intolerance. Identifying derogatory comments that ridicule or belittle the user is a complex problem. the process of manually identifying involves the examination of individual paragraphs and their subsequent categorization. the process of comments filtering typically entails the involvement of human reviewers. the duration of this procedure is relatively lengthy. Automated software is necessary for the purpose of detecting inappropriate speech. Various methodologies in machine learning and natural language processing can be employed for the automated de-tection of obscenities. the objective is to predict the offensive rating of a text that has been classified as such. the rating is assigned using a numerical scale rang-ing from 0 to 5. the purpose of this analysis is to ascertain the level of offensive-ness that a given text may have on the broader population.

关键词： Deep learning Social networking (online) Text recognition Filtering Focusing Market research Software Natural language processing Surges Cybernetics

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Revocable Identity-Based Searchable Encryption Scheme for Smart Grid

Revocable Identity-Based Searchable Encryption Scheme for Sm...

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Natural language processing (ICNLP), international conference on

作者： Aqian Wang Baodong Qin Yajun Fan School of Cyberspace Security Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In view of the problem that the current smart grid privacy protection technology cannot provide sufficient efficiency and security, and user privacy data is prone to leakage or damage during data transmission and storage with service nodes, a method is designed that not only protects user privacy but also supports solutions for retrieving and processing information, using identity-based searchable encryption to simplify certificate management and reduce computational overhead. However, adversarys pretending to be legitimate users and transmitting malicious information to service nodes (such as power suppliers) can also interfere with the normal operation of smart grids. In order to resist leakage, the retrieval permissions of malicious users need to be revoked, and the revoked users lose the ability to retrieve previous power data. therefore, a revocable identity-based searchable encryption scheme is proposed, while ensuring the forward security of ciphertext. the scheme has good computational efficiency in both the encryption and testing phases, and can provide privacy for data transmitted in smart grids and provide security protection.

关键词： Data privacy Resists Search problems Natural language processing Smart grids Encryption Computational efficiency Data communication Protection Testing

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Changing Topics for Changing Times: thematic and Temporal-Based Analysis of the Philippine Senatorial and Midterm Elections 6

Changing Topics for Changing Times: Thematic and Temporal-Ba...

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6th international conference on Natural language processing and information Retrieval, NLPIR 2022

作者： Cruz, Lamar Clarence Dela Cruz, Jessica Nicole Maglangit, Shane Francis Magtira, Mico Imperial, Joseph Marvin Rodriguez, Ramon College of Computing and Information Technologies National University-Manila Manila Philippines

ISBN: (纸本)9781450397629

Elections in the Philippines are one of the most prominent events in the country. It is a time when people choose their leader through the democratic election process. During the electoral campaign, candidates try several strategies to win the election. However, victors emerge are those familiar and have mass-based support. In this paper, we have investigated the perception of netizens in the Philippines for the last two election seasons, 2019 and 2022, based on Twitter data. thematic analyses were conducted to determine the themes related to winning candidates and create a link on how they may affect their election success. the study found that the top candidates (Cynthia Villar in 2019 and Robinhood Padilla in 2022) mainly received criticism and opposition towards their campaigns from the themes. Moreover, findings suggest that candidates with more negative or positive publicity emerge as winners. Long-time politicians and organized groups endorsed most winners during the 2019 and 2022 elections. © 2022 ACM.

关键词： Natural language processing systems

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Trademark Detection Based on Improved SSD Algorithm 6

Trademark Detection Based on Improved SSD Algorithm

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6th international conference on Machine Learning and Natural language processing, MLNLP 2023

作者： Chen, Xun Bi, Haidong Hu, Chao School of Information and Communication Engineering Hainan University China Big Data Institute Central South University China

ISBN: (纸本)9798400709241

Trademarks are found everywhere in daily life. With the development of deep learning in the field of image recognition, trademark detection has become a hot research topic. Although trademark detection has made great progress, there are still some issues that need to be addressed. this paper proposes a trademark detection algorithm based on improved SSD model to address the challenge of low precision in detecting small-sized trademark images. In order to address the issue of excessive parameters in the original SSD network, the lightweight network Darknet is adopted to replace the feature extraction network in SSD. this reduces the number of network parameters and enhances the speed of network detection. To tackle the challenging detection of small objects, an improved FPN network is employed. It progressively fuses multi-scale feature maps from the deep to shallow layers of the network in SSD, aiming to enrich information across all prediction layers to maximize the detection accuracy of small objects. As for the mismatch between the prior box mechanism of the original SSD and the detection of small object datasets, the K-means++ algorithm is utilized to determine the sizes of anchor boxes and obtain appropriate aspect ratios for the current dataset. this enhances both the efficiency and accuracy of the model's detection capability. Experimental results show that compared with the original SSD algorithm, the improved SSD detection algorithm in this paper has better detection accuracy, and the average detection average is increased by 9.8% which proves the effectiveness of the proposed model. © 2023 ACM.

关键词： Image enhancement

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BWSC: BERT-Based N-Gram Enhanced Word Segmentation for Chinese

BWSC: BERT-Based N-Gram Enhanced Word Segmentation for Chine...

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Frontiers technology of information and Computer (ICFTIC), IEEE international conference on

作者： Hua Wei Xu Guo School of Electronics Information Engineering Shanghai Dianji University Shanghai China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

the word segmentation is a key fundamental task in the field of natural language processing (NLP) and a prerequisite for advanced tasks such as machine translation, sentiment analysis and language recognition. In Chinese language processing, the difficulty of word segmentation is particularly prominent. Because Chinese is based on characters, there are no symbols such as spaces between words to indicate the boundary of words, and “words” are the smallest meaningful language components that can act independently. In practical applications, the semantics of a text are not only dependent on a single word, but also closely related to the larger granularity of the language structure. In the pre-training stage of the model, text is usually divided into small-grained units such as characters or lexical blocks for modeling, and it is easy to ignore the information carried by larger-grained text structures, which may lead to the loss of important semantic information, and the results of word segmentation directly affect the accuracy of subsequent NLP tasks, such as parts-of-speech tagging, named entity recognition, and syntax analysis. In order to improve the accuracy of Chinese word segmentation (CWS), a new model called BERT-based N-gram enhanced word segmentation for Chinese (BWSC) is proposed in this paper. the model combines the contextual coding capabilities of the BERT pre-trained model and is pre-processed by the Jieba word segmentation tool to provide consistent word segmentation criteria. Jieba not only ensures the consistency of word segmentation, but also can automatically identify unregistered words (such as new words and proper nouns), to improve the recognition and processing ability of special words. BWSC enhances the comprehensive expression of character sequences by introducing N-gram features, so that the model can capture the relationship between characters, words and phrases. the Fl score of BWSC in SIGHAN2005 dataset reaches 96.63%. the experimental res

关键词： Analytical models Sentiment analysis Accuracy Semantics Symbols Named entity recognition Tagging Syntactics Context modeling Periodic structures

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A Stylometric Dataset for Bengali Poems 6

A Stylometric Dataset for Bengali Poems

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6th international conference on Natural language processing and information Retrieval, NLPIR 2022

作者： Shuhan, Mirza Kamrul Bashar Dey, Rupasree Saha, Sourav Anjum, Md Shafa Ul Zaman, Tarannum Shaila Software Research and Engineering BKash Limited Dhaka Bangladesh Computer Science Oregon State University CorvallisOR United States Business Administration Shahjalal University of Science and Technology Sylhet Bangladesh Progoti Systems Limited Dhaka Bangladesh Suny Polytechnic Institute UticaNY United States

ISBN: (纸本)9781450397629

Poetry is a form of literature that conveys feelings using different styles, aesthetics, and rhythms. the Bengali language has an enriched collection of poems. Every poet has an individual style of expressing their thoughts and emotions. However, stylometric research in this branch of the Bengali language is still in its early stage of development. In this paper, we have presented a stylometric dataset, which has 6,070 poems of 137 poets stored in the textual format. To the best of our knowledge, this is the first stylometric dataset for Bengali poems which will add an extra dimension to the expanding research arena of the Bengali language. To explore the usability of this dataset, we developed poem genre classifiers using deep learning that can classify these poems. Performance analysis of some deep learning classifiers has been presented in addition to classification. the classifiers include GRU and CNN. Among these two, GRU showed better performance by 91.48 in terms of the F1-score. the dataset will be publicly available at https://***/shuhanmirza/Bengali-Poem-Dataset after publishing this article. © 2022 ACM.

关键词： Classification (of information)

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Towards Completeness: A Generalizable Action Proposal Generator for Zero-Shot Temporal Action Localization 27th

Towards Completeness: A Generalizable Action Proposal Genera...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Du, Jia-Run Lin, Kun-Yu Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031784439

To address the zero-shot temporal action localization (ZSTAL) task, existing works develop models that are generalizable to detect and classify actions from unseen categories. they typically develop a category-agnostic action detector and combine it with the Contrastive language-Image Pre-training (CLIP) model to solve ZSTAL. However, these methods suffer from incomplete action proposals generated for unseen categories, since they follow a frame-level prediction paradigm and require hand-crafted post-processing to generate action proposals. To address this problem, in this work, we propose a novel model named Generalizable Action Proposal generator (GAP), which can interface seamlessly with CLIP and generate action proposals in a holistic way. Our GAP is built in a query-based architecture and trained with a proposal-level objective, enabling it to estimate proposal completeness and eliminate the hand-crafted post-processing. Based on this architecture, we propose an Action-aware Discrimination loss to enhance the category-agnostic dynamic information of actions. Besides, we introduce a Static-Dynamic Rectifying module that incorporates the generalizable static information from CLIP to refine the predicted proposals, which improves proposal completeness in a generalizable manner. Our experiments show that our GAP achieves state-of-the-art performance on two challenging ZSTAL benchmarks, i.e., thumos14 and ActivityNet1.3. Specifically, our model obtains significant performance improvement over previous works on the two benchmarks, i.e., +3.2% and +3.4% average mAP, respectively. the code is available at https://***/Run542968/GAP. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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