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检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15205 条记录，以下是4861-4870 订阅

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ADAL-GCN: Action Description Aided Learning Graph Convolution Network for Early Action Prediction 7th

ADAL-GCN: Action Description Aided Learning Graph Convolutio...

引用

7th Chinese conference on Pattern Recognition and Computer Vision

作者： Li, Xianshan Dong, Yuan Ning, Xingxing Zhang, Pengwei Zhao, Fengda Yanshan Univ Qinhuangdao Hebei Peoples R China Xinjiang Univ Sci & Technol Sch Informat Sci & Engn Urumqi Xinjiang Peoples R China Yanshan Univ Key Lab Software Engn IIebei Prov Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9789819787944;9789819787951

Early human action prediction aims to complete the prediction of complete action sequences based solely on initial action sequences acquired at an initial stage. Considering that the execution of a single action usually relies on the synergistic coordination of multiple key body parts and the movement amplitude of different body parts at the onset of an action varies minimally, early human action prediction demonstrates high sensitivity to the location of action initiation and the type of action. Currently, skeletal-based action prediction methods primarily focus on action classification and exhibit limited capability for discrimination in terms of semantic association between actions. For instance, distinguishing actions concentrated on elbow joint movements, such as "touching the neck" and "touching the head," proves challenging through classification alone but can be achieved through semantic relationships. Therefore, when differentiating similar actions, incorporating descriptions of specific joint movements can enhance the feature extraction ability of the model. This paper introduces an Action Description-Assisted Learning Graph Convolutional Network (ADAL-GCN), which utilizes large language models as knowledge engines to pre-generate descriptions for key parts of different actions. These descriptions are then transformed into semantically rich feature vectors through text encoding. Furthermore, the model adopts a lightweight design, decoupling features across channel and temporal dimensions, consolidating redundant network modules, and executing strategic computational migration to optimize processing efficiency. Experimental results demonstrate significant performance improvements achieved by our proposed method, which achieves substantial reductions in training time without additional computational overhead.

关键词： Early Action Prediction Large language Model Skeleton Encoder Text Encoder Lightweight Design

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Prosody: Models, methods and Applications 59

Prosody: Models, Methods and Applications

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint conference on natural language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Ward, Nigel G. Levow, Gina-Anne Univ Texas El Paso El Paso TX 79968 USA Univ Washington Seattle WA 98195 USA

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natural language processing Enhancing Human-Computer Interaction for Disabled

Natural Language Processing Enhancing Human-Computer Interac...

引用

Progressive Innovations in Intelligent Systems and Data Science (ICPIDS), International conference on

作者： Partho Protim Roy Pushkal Anand Shivam Tiwari Bharat Bhushan Department of Computer Science & Engineering Sharda University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Such as physical interaction, individuals with disabilities often encounter barriers when accessing and interacting with traditional computer interfaces. Navigating the digital interface poses challenges for those with disabilities. The necessity for innovative solutions is underscored by the main issue of limited accessibility to computer interfaces for individuals with disabilities. At present time, Human-Computer Interaction technology is most known in the tech industry. Which enables any individual to interact with system or computer. Here, natural language processing plays a major component in the advancement of Human-Computer Interaction technology. This paper surveys recent methods, techniques like embedding, Word2Vec and technology to help in improvement of the Human-Computer Interaction. This work highlights the need of integration of advance natural language processing like with Human-Computer Interaction for people with disabilities. This paper also proposes work on a hands-free device navigation system or application for differently abled peoples.

关键词： Human computer interaction Surveys Computer interfaces Technological innovation Navigation Education Semantics natural language processing Real-time systems Sustainable development

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natural language processing for clusterization of genes according to their functions

Natural language processing for clusterization of genes acco...

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2022 Ural-Siberian conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022

作者： Dordiuk, Vladislav Demicheva, Ekaterina Espino, Fernando Polanco Ushenin, Konstantin Institute of Immunology and Physiology Ekaterinburg Russia Ural Federal University Ekaterinburg Russia

ISBN: (数字)9781665452885

ISBN: (纸本)9781665452885

There are hundreds of methods for analysis of data obtained in mRNA-sequencing. The most of them are focused on small number of genes. In this study, we propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The list of genes is enriched with information from open databases. Then, the descriptions are encoded as vectors using the pretrained language model (BERT) and some text processing approaches. The encoded gene function pass through the dimensionality reduction and clusterization. Aiming to find the most efficient pipeline, 180 cases of pipeline with different methods in the major pipeline steps were analyzed. The performance was evaluated with clusterization indexes and expert review of the results. © 2022 IEEE.

关键词： Gene expression

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An empirical study on the impact of technological innovation on enterprise performance based on IT value-added theory

An empirical study on the impact of technological innovation...

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2022 European conference on natural language processing and Information Retrieval, ECNLPIR 2022

作者： Yuan, Jingxue Teoh, Aiping Chen, Wanti Guangzhou College of Technology and Business Guangzhou China Universiti Sains Malaysia Penang Graduate School of Business Malaysia Guangdong Shungao Investment and Financing Guarantee Co Ltd Foshan China

ISBN: (纸本)9781665473828

With major changes in productivity and the continuous upgrading of consumer demand, it is no longer sustainable for businesses to compete on the basis of low-cost, low-value-added products or services. As a result, it is becoming increasingly important to accelerate the integration of internal and external resources via information management means and systems, reshaping the company's business process, manufacturing model, organizational structure, and so on, in order to fundamentally improve the company's innovation and creation ability and thus build the core momentum of the enterprise's sustainable development. Improving the ability of scientific decision support and improving the innovative attributes and market competitiveness of products are conducive to improving enterprises' innovation capability and financial situation. The IT value-added theory applies the cost-benefit principle to information capital, and there is a relationship between information technology input, technological innovation ability, and enterprise performance. This paper primarily demonstrates the positive nature of this relationship by introducing relevant variables. As a result, it provides strong support and reference value for the future information layout and innovation capability of industrial and high-tech enterprises in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), as well as the enterprise performance dividend it brings. © 2022 IEEE.

关键词： Information management

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Multiple Feature Fusion Based on Hierarchical Constraint for Crack Detection

Multiple Feature Fusion Based on Hierarchical Constraint for...

引用

natural language processing (ICNLP), International conference on

作者： Junpeng Zhang Weitao Zhang Jie Fang School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Because of their security and convenience, crack detection methods based on image processing technique (IPT) have gradually become a mainstream trend in this field. However, the performances of existing methods can not satisfy the practical applications for serious category imbalance and uncontrolled noisy interferences. In this paper, a multiple feature fusion framework based on hierarchical constraint for crack detection is proposed, which mainly contains a weight-shared encoder component and three independent decoder ones, and the outputs of these three decoder components are fused together to finalize the detection. Specifically, each decoder component is constrained by pseudo label generated from the true crack map with a specific expansion stride, which can alleviate the serious category imbalance problem and avoid the risk of model collapse. Furthermore, the fused feature can represent the road image well because it can ensure the existence and meanwhile avert the false-alarm of crack samples. In addition, the backbone of our framework is based on a lightweight UNet equipped with enhancement modules, which can increase the structural representation capability, decrease the computational load, and speed up the reasoning time simultaneously. Finally qualitative and quantitative experimental results on three challenging datasets have verified the superiority of the proposed framework.

关键词： Roads Feature extraction Market research natural language processing Cognition Decoding Security Noise measurement

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Can language Models be Biomedical Knowledge Bases?

Can Language Models be Biomedical Knowledge Bases?

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2021 conference on empirical methods in natural language processing, EMNLP 2021

作者： Sung, Mujeen Lee, Jinhyuk Yi, Sean S. Jeon, Minji Kim, Sungdong Kang, Jaewoo Korea University Korea Republic of Princeton University United States Icahn School of Medicine at Mount Sinai United States NAVER AI Lab

ISBN: (纸本)9781955917094

Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs contain and how we can extract that knowledge, treating LMs as knowledge bases (KBs). While there has been much work on probing LMs in the general domain, there has been little attention to whether these powerful LMs can be used as domain-specific KBs. To this end, we create the BIOLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs. We find that biomedical LMs with recently proposed probing methods can achieve up to 18.51% Acc@5 on retrieving biomedical knowledge. Although this seems promising given the task difficulty, our detailed analyses reveal that most predictions are highly correlated with prompt templates without any subjects, hence producing similar results on each relation and hindering their capabilities to be used as domain-specific KBs. We hope that BIOLAMA can serve as a challenging benchmark for biomedical factual probing. © 2021 Association for Computational Linguistics

关键词： Computational linguistics

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Membership Inference Attacks against Fine-tuned Large language Models via Self-prompt Calibration 38

Membership Inference Attacks against Fine-tuned Large Langua...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Fu, Wenjie Wang, Huandong Gao, Chen Liu, Guanghua Li, Yong Jiang, Tao Huazhong University of Science and Technology China Tsinghua University China

Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not. Existing MIAs designed for large language models (LLMs) can be bifurcated into two types: reference-free and reference-based attacks. Although reference-based attacks appear promising performance by calibrating the probability measured on the target model with reference models, this illusion of privacy risk heavily depends on a reference dataset that closely resembles the training set. Both two types of attacks are predicated on the hypothesis that training records consistently maintain a higher probability of being sampled. However, this hypothesis heavily relies on the overfitting of target models, which will be mitigated by multiple regularization methods and the generalization of LLMs. Thus, these reasons lead to high false-positive rates of MIAs in practical scenarios. We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA). Specifically, we introduce a self-prompt approach, which constructs the dataset to fine-tune the reference model by prompting the target LLM itself. In this manner, the adversary can collect a dataset with a similar distribution from public APIs. Furthermore, we introduce probabilistic variation, a more reliable membership signal based on LLM memorization rather than overfitting, from which we rediscover the neighbour attack with theoretical grounding. Comprehensive evaluation conducted on three datasets and four exemplary LLMs shows that SPV-MIA raises the AUC of MIAs from 0.7 to a significantly high level of 0.9. Our code and dataset are available at: https://***/tsinghua-fib-lab/NeurIPS2024_SPV-MIA. © 2024 Neural information processing systems foundation. All rights reserved.

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Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends 37

Beyond Myopia: Learning from Positive and Unlabeled Data thr...

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37th conference on Neural Information processing Systems, NeurIPS 2023

作者： Wang, Xinrui Wan, Wenhai Geng, Chuanxing Li, Shaoyuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

ISBN: (纸本)9781713899921

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns. Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to 11.3% in key metrics. The code is available at https://***/wxr99/HolisticPU. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Iterative methods

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YouTube Video Summarizer Using ASR

YouTube Video Summarizer Using ASR

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Blockchain and Distributed Systems Security (ICBDS), IEEE International conference on

作者： Ratna Patil Amar Buchade Gitanjali Yadav Nakul Sharma Sarthak Joshi Anushka Bhokare Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Platforms like YouTube have revolutionized the way people distribute and consume information in the video-dominated digital age. However, viewers who are trying to get pertinent information quickly may find it difficult due to the sheer volume of videos. This research describes a system for summarizing YouTube videos that effectively extracts information using Term Frequency-Inverse Document Frequency (TF-IDF) and Automatic Speech Recognition (ASR) analysis. ASR translates audio to text, and TF-IDF finds the most important information for brief summaries. empirical validation supports the optimization of usability through automation and personalization. The technology facilitates navigation through large amounts of video footage by supporting natural language processing (NLP) and information retrieval.

关键词： Video on demand Translation Text summarization natural language processing User experience Web sites Data mining Security Usability Videos

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