检索结果-内蒙古大学图书馆

Improving Rumor Detection by Image Captioning and Multi-Cell Bi-RNN With Self-Attention in Social Networks

INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 2022年第1期18卷 1页

作者： Wang, Jenq-Haur Huang, Chin-Wei Norouzi, Mehdi Natl Taipei Univ Technol Taipei Taiwan Univ Cincinnati Cincinnati OH USA

User-generated contents in social media are not verified before being posted. They could bring many problems if they were misused. Among various types of rumors, the authors focus on the type in which there's mismatch between images and their surrounding texts. They can be detected by multimodal feature fusion in RNNs with attention mechanism, but the relations between images and texts are not well-addressed. In this paper, the authors propose to improve rumor detection by image captioning and RNNs with self-attention. Firstly, they utilize the idea of image captioning to translate images into the corresponding text descriptions. Secondly, these caption words are represented by word embedding models and aggregated with surrounding texts using early fusion. Finally, multi-cell bidirectional RNNs with self-attention are used to learn important features to identify rumors. From the experimental results, the best F-measure of 0.882 can be obtained, which shows the potential of our proposed approach to rumor detection. Further investigation is needed for data in larger scale.

关键词： Bi-Directional Gated Recurrent Unit Multimodal Feature Fusion Rumor Detection Image Captioning Self-Attention Mechanism sequence-to-sequence model

来源：评论

学校读者我要写书评

暂无评论

TransSounder: A Hybrid TransUNet-TransFuse Architectural Framework for Semantic Segmentation of Radar Sounder Data

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Ghosh, Raktim Bovolo, Francesca Fdn Bruno Kessler Ctr Digital Soc I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

Radar sounders (RSs) are nadir-looking sensors operating in high frequency (HF) or very high frequency (VHF) bands that profile subsurface targets to retrieve miscellaneous scientific information. Due to the complex electromagnetic interaction between backscattered returns, the interpretation of RS data is challenging. The investigations of ice-sheet subsurface structures require automatic techniques to account for both the sequential spatial distribution of subsurface targets and relevant statistical properties embedded in RS signals. Automatic techniques exist for characterizing these targets either related to probabilistic inference models or convolutional neural network (CNN) deep learning methods. Unfortunately, CNN-based methods capture local spatial context and merely model the global spatial context. In contrast to CNN, the transformer-based models are reliable architectures for capturing long-range sequence-to-sequence global spatial contextual prior. Motivated by the aforementioned fact, we propose a novel transformer-based semantic segmentation architecture named TransSounder to effectively encode the sequential structures of the RS signals. The TransSounder was constructed on a hybrid TransUNet-TransFuse architectural framework to systematically augment the modules from TransUNet and TransFuse architectures. Experimental results obtained using the Multichannel Coherent Radar Depth Sounder (MCoRDS) dataset confirms the robustness and capability of transformers to accurately characterize the different subsurface targets.

关键词： Ice Transformers Feature extraction Probabilistic logic Convolutional neural networks Decoding Convolution Multichannel Coherent Radar Depth Sounder (MCoRDS) radar sounder (RS) semantic segmentation sequence-to-sequence model transformers TransFuse TransUNet

来源：评论

学校读者我要写书评

暂无评论

Prediction of estimated time of arrival for multi-airport systems via "Bubble"mechanism

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年第1期149卷

作者： Wang, Lechen Mao, Jianfeng Li, Lishuai Li, Xuechun Tu, Yilei Chinese Univ Hong Kong Sch Data Sci Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Guangdong Peoples R China Shenzhen Res Inst Big Data Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Guangdong Prov Key Lab Big Data Comp Shenzhen Guangdong Peoples R China Delft Univ Technol Fac Aerosp Engn NL-2600 AA Delft Netherlands City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Chinese Univ Hong Kong Sch Data Sci 2001 Longxiang Blvd Shenzhen 518172 Guangdong Peoples R China

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel "Bubble"mechanism to accurately predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel time of an origin-destination (OD) pair is decomposed into two stages, termed as out-MAS and in-MAS stages. For the out-MAS stage, Auto-Regressive Integrated Moving Average (ARIMA) is used to predict the travel time of a flight to reach the MAS boundary. For the in-MAS stage, we construct new spatio-temporal features based on clustering analysis of trajectory patterns facilitated by a novel data-driven hybrid polar sampling method. A sequence-to-sequence prediction model, Multi-variate Stacked Fully connected Bidirectional Long-Short Term Memory, is further developed to achieve multi-step-ahead predictions of in-MAS travel time for each trajectory pattern using the spatio-temporal features as input. Finally, the medium-term ETA prediction for an MAS is achieved by integrating the out-MAS and in-MAS prediction with the help of trajectory pattern prediction via random forest. A case study of predicting medium-term ETA for a typical MAS in China, Guangdong-Hong Kong-Macao Greater Bay Area, is conducted to demonstrate the usage and promising performance of the proposed method in comparison to several commonly used end-to-end learning methods.

关键词： Multi-airport systems Flight estimated time of arrival Medium-term prediction Trajectory pattern clustering sequence-to-sequence model Spatio-temporal features

来源：评论

学校读者我要写书评

暂无评论

Onoma-to-wave: Environmental Sound Synthesis from Onomatopoeic Words

引用

APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2022年第1期11卷

作者： Okamoto, Yuki Imoto, Keisuke Takamichi, Shinnosuke Yamanishi, Ryosuke Fukumori, Takahiro Yamashita, Yoichi Ritsumeikan Univ Shiga Japan Doshisha Univ Kyoto Japan Univ Tokyo Tokyo Japan Kansai Univ Osaka Japan

In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words. As one way of expressing an environmental sound, we can use an onomatopoeic word, which is a character sequence for phonetically imitating a sound. An onomatopoeic word is effective for describing diverse sound features. Therefore, the use of onomatopoeic words as input for environmental sound synthesis will enable us to generate diverse sounds. To generate diverse sounds, we propose a method based on a sequence-to-sequence framework for synthesizing environmental sounds from onomatopoeic words. We also propose a method of environmental sound synthesis using onomatopoeic words and sound event labels. The use of sound event labels in addition to onomatopoeic words enables us to capture each sound event's feature depending on the input sound event label. Our subjective experiments show that our proposed methods achieve higher diversity and naturalness than conventional methods using sound event labels.

关键词： Environmental sound synthesis sound event onomatopoeic word sequence-to-sequence model

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based household electric energy consumption forecasting

引用

JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 639-642页

作者： Hyeon, Jonghwan Lee, HyeYoung Ko, Bowon Choi, Ho-Jin Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

With the advent of various electronic products, the household electric energy consumption is continuously increasing, and therefore it becomes very important to predict the household electric energy consumption accurately. Energy prediction models also have been developed for decades with advanced machine learning technologies. Meanwhile, the deep learning models are still actively under study, and many newer models show the state-of-the-art performance. Therefore, it would be meaningful to conduct the same experiment with these new models. Here, the authors predict the household electric energy consumption using deep learning models, known to be suitable for dealing with time-series data. Specifically, vanilla long short-term memory (LSTM), sequence to sequence, and sequence to sequence with attention mechanism are used to predict the electric energy consumption in the household. As a result, the vanilla LSTM shows the best performance on the root-mean-square error metric. However, from a graphical point of view, it seems that the sequence-to-sequence model predicts the energy consumption patterns best and the vanilla LSTM does not follow the pattern well. Also, to achieve the best performance of each deep learning model, vanilla LSTM, sequence to sequence, and sequence to sequence with attention mechanism should observe past 72, 72, and 24 h, respectively.

关键词： power consumption time series learning (artificial intelligence) mean square error methods load forecasting power engineering computing recurrent neural nets vanilla LSTM deep learning model energy prediction models advanced machine learning technologies sequence-to-sequence model energy consumption patterns household electric energy consumption forecasting time-series data vanilla long short-term memory root-mean-square error metric sequence to sequence with attention mechanism time 72 0 hour time 24 0 hour

来源：评论

学校读者我要写书评

暂无评论

Patching as translation: the data and the metaphor 20

Patching as translation: the data and the metaphor

引用

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Yangruibo Ding Baishakhi Ray Premkumar Devanbu Vincent J. Hellendoorn Columbia University University of California

ISBN: (纸本)9781450367684

Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model's initial success at a given task does not necessarily mean it is well-suited for the task. In this work, we examine a common example of this phenomenon: the conceit that "software patching is like language translation". We demonstrate empirically that there are subtle, but critical distinctions between sequence-to-sequence models and translation model: while program repair benefits greatly from the former, general modeling architecture, it actually suffers from design decisions built into the latter, both in terms of translation accuracy and diversity. Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions. Our findings also lend strong support to the recent trend towards synthesizing edits of code conditional on the buggy context, to repair bugs. We implement such models ourselves as "proof-of-concept" tools and empirically confirm that they behave in a fundamentally different, more effective way than the studied translation-based architectures. Overall, our results demonstrate the merit of studying the intricacies of machine learned models in software engineering: not only can this help elucidate potential issues that may be overshadowed by increases in accuracy; it can also help innovate on these models to raise the state-of-the-art further. We will publicly release our replication data and materials at https://***/ARiSE-Lab/Patch-as-translation.

关键词： big code automated program repair neural machine translation sequence-to-sequence model

来源：评论

学校读者我要写书评

暂无评论

FORWARD ATTENTION IN sequence-TO-sequence ACOUSTIC modelING FOR SPEECH SYNTHESIS

FORWARD ATTENTION IN SEQUENCE-TO-SEQUENCE ACOUSTIC MODELING ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jing-Xuan Zhang Zhen-Hua Ling Li-Rong Dai National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei P.R.China

ISBN: (纸本)9781538646595

This paper proposes a forward attention method for the sequence-to-sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment from phone sequences to acoustic sequences. Only the alignment paths that satisfy the monotonic condition are taken into consideration at each decoder timestep. The modified attention probabilities at each timestep are computed recursively using a forward algorithm. A transition agent for forward attention is further proposed, which helps the attention mechanism to make decisions whether to move forward or stay at each decoder timestep. Experimental results show that the proposed forward attention method achieves faster convergence speed and higher stability than the baseline attention method. Besides, the method of forward attention with transition agent can also help improve the naturalness of synthetic speech and control the speed of synthetic speech effectively.

关键词： sequence-to-sequence model encoder-decoder attention speech synthesis Speech synthesis acoustic modeling decoders Telephone modeling

来源：评论

学校读者我要写书评

暂无评论

END-TO-END MULTIMODAL SPEECH RECOGNITION

END-TO-END MULTIMODAL SPEECH RECOGNITION

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shruti Palaskar Ramon Sanabria Florian Metze Carnegie Mellon University Pittsburgh PA U.S.A.

ISBN: (纸本)9781538646595

Transcription or sub-titling of open-domain videos is still a challenging domain for Automatic Speech Recognition (ASR) due to the data's challenging acoustics, variable signal processing and the essentially unrestricted domain of the data. In previous work, we have shown that the visual channel - specifically object and scene features - can help to adapt the acoustic model (AM) and language model (LM) of a recognizer, and we are now expanding this work to end-to-end approaches. In the case of a Connectionist Temporal Classification (CTC)-based approach, we retain the separation of AM and LM, while for a sequence-to-sequence (S2S) approach, both information sources are adapted together, in a single model. This paper also analyzes the behavior of CTC and S2S models on noisy video data (How-To corpus), and compares it to results on the clean Wall Street Journal (WSJ) corpus, providing insight into the robustness of both approaches.

关键词： Audiovisual Speech Recognition Connectionist Temporal Classification sequence-to-sequence model Adaptation Speech recognition recognizer Acoustic models Information source signal processing Open domains Genetic Transcription modelling languages

来源：评论

学校读者我要写书评

暂无评论

Prediction of mine water quality by the Seq2Seq model based on attention mechanism

引用

Heliyon 2024年第18期10卷 e37916页

作者： Wang, Xiaolong Li, Yang CHN Shendong Coal Group Co. LTD. Shenmu 719300 China Summit Technologies Co. LTD Xian 710000 China

In recent years, as China's industrialization level has advanced, the issue of environmental pollution, particularly mine water pollution, has become increasingly severe. Water quality prediction is a fundamental aspect of water resource protection and a critical approach to addressing the water resource crisis. For improvement in water quality prediction, this research first analyzes the characteristics of mine water quality changes and provides a brief overview of water quality prediction. Subsequently, the Long Short-Term Memory and sequence to sequence (Seq2Seq) models, derived from Artificial Neural Networks, are introduced. The Seq2Seq water quality prediction model is implemented, incorporating the attention mechanism. Experimental validation confirms the effectiveness of the proposed model. The results demonstrate that the attention mechanism-based Seq2Seq model accurately predicts parameters such as pH value, Dissolved Oxygen, ammonia nitrogen, and Chemical Oxygen Demand, exhibiting a high degree of consistency with actual results. They play a vital role in assessing the health of the water and its ability to support aquatic life. The change of these indicators can reflect the degree and type of water pollution. Moreover, the Seq2Seq + attention model stands out with the lowest predicted Root Mean Square Error of 0.309. Notably, in comparison to the traditional Seq2Seq model, the incorporation of attention mechanisms in the Seq2Seq model results in a substantial 2.94 reduction in Mean Absolute Error. This research on the Seq2Seq water quality prediction model with attention mechanism provides valuable insights and references for future endeavors in water quality prediction. © 2024 The Authors

关键词： Artificial neural network Attention mechanism sequence-to-sequence model Water quality prediction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：