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Performance Comparison of Neural Networks for HRTFs Approximation

高技术通讯（英文版） 2000年第1期6卷 14-17页

作者： Zhu Xiaoguang Hong Bingrong Wang Dongmu Department of Computer Science and Engineering Harbin Institute of Technology Harbin 150001 P. R. China Beijing Simulation Center Beijing 100854 P. R. China

In order to approach to head-related transfer functions (HRTFs), this paper employs and compares three kinds of one-input neural network models, namely, multi-layer perceptron (MLP) networks, radial basis function (RBF) networks and wavelet neural networks (WNN) so as to select the best network model for further HRTFs approximation. Experimental results demonstrate that wavelet neural networks are more efficient and useful.

关键词： Multi-layer perceptron (MLP) Radial basis function (RBF) networks Wavelet neural networks (WNN) Head-related transfer functions (HRTFs)

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A Multi-objective optimization model for simulation-based acquisition 7

A Multi-objective optimization model for simulation-based ac...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Zhang, Xue Xue, Junjie Zhou, Junhua Shi, Guoqiang Yu, Yan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing Simulation Center Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (纸本)9781728170862

Large-scale equipment presents the characteristics of technology-intensive, knowledge-intensive and complex, which brings high investment and high risk to equipment acquisition. This paper analyzes the various components of equipment acquisition to fully apply modeling and simulation technology to each stage of it. On the basis of multi-objective optimization theory, the objective function of the lowest risk and lowest cost of simulation-based acquisition (SBA) is proposed. Then establishing a multi-objective optimization model for simulation-based acquisition according to the corresponding constraints. The nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) algorithms are used to optimize the model, and the majority voting method is used for integration. The comparison of simulation examples proves that the multi-objective optimization model oriented to SBA designed in this paper can effectively realize the optimization of each objective of SBA under cost constraints. And the feasibility and efficiency of optimization solution are guaranteed. © 2020 IEEE.

关键词： Multiobjective optimization

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Identifying suspected cybermob on Tieba 15th

Identifying suspected cybermob on Tieba

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15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016

作者： Shi, Shumin Zhou, Xinyu Zhao, Meng Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783319476735

This paper describes an approach to identify suspected cybermob on social media. Many researches involve making predictions of group emotion on Internet (such as quantifying sentiment polarity), but this paper instead focuses on the origin of information diffusion, namely back to its makers and contributors. According our previous findings that have shown, at the level of Tieba’s contents, the negative information or emotions spread faster than positive ones, we centre on the maker of negative message in this paper, so-called cybermobs who post aggressive, provocative or insulting remarks on social websites. We explore the different characteristics between suspected cybermobs and general netizens and then extract relative unique features of suspected cybermobs. We construct real system to identify suspected cybermob automatically using machine learning method with above features, including other common features like user/content-based ones. Empirical results show that our approach can detect suspected cybermob correctly and efficiently as we evaluate it with benchmark models, and apply it to actual cases. © Springer International Publishing AG 2016.

关键词： Artificial intelligence

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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension 29

ET5: A Novel End-to-end Framework for Conversational Machine...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhang, Xiao Huang, Heyan Chi, Zewen Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China

Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning;(2) span extraction if required by the above decision;(3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Decision making

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BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity 11

BIT at SemEval-2017 Task 1: Using Semantic Information Space...

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11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Wu, Hao Huang, Heyan Jian, Ping Guo, Yuhang Su, Chao Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9781945626555

This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ the unsupervised one. All our systems mainly depend on the semantic information space (SIS), which is constructed based on the semantic hierarchical taxonomy in WordNet, to compute non-overlapping information content (IC) of sentences. Our team ranked 2nd among 31 participating teams by the primary score of Pearson correlation coefficient (PCC) mean of 7 tracks and achieved the best performance on Track 1 (AR-AR) dataset. © 2017 Association for Computational Linguistics

关键词： Semantics

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Concept pointer network for abstractive summarization 9

Concept pointer network for abstractive summarization

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2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019

作者： Wenbo, Wang Yang, Gao Heyan, Huang Yuxiang, Zhou School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9781950737901

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework. © 2019 Association for Computational Linguistics

关键词： Semantics

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Temporal enhanced sentence-level attention model for hashtag recommendation

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CAAI Transactions on Intelligence technology 2018年第2期3卷 95-100页

作者： Jun Ma Chong Feng Ge Shi Xuewen Shi Heyang Huang Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications College of Computer Science Beijing Institute of Technology University 5 South Zhong Guan Cun Street of Haidian District Beijing People's Republic of China

Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.

关键词：分类器数据集计算机技术智能技术

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Revisiting constraints on proton PDFs from HERA DIS, Drell-Yan, W and Z boson production, and projected EIC measurements

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Physical Review D 2025年第3期111卷 034023-034023页

作者： Majid Azizi Maryam Soleymaninia Hadi Hashamipour Maral Salajegheh Hamzeh Khanpour Ulf-G. Meißner School of Particles and Accelerators Institute for Research in Fundamental Sciences (IPM) P.O. Box 19395-5531 Tehran Iran Istituto Nazionale di Fisica Nucleare Gruppo collegato di Cosenza I-87036 Arcavacata di Rende Cosenza Italy Helmholtz-Institut für Strahlen-und Kernphysik and Bethe Center for Theoretical Physics Universität Bonn D-53115 Bonn Germany AGH University Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 30-055 Krakow Poland Department of Physics University of Science and Technology of Mazandaran P.O. Box 48518-78195 Behshahr Iran Institute for Advanced Simulation (IAS-4) Forschungszentrum Jülich D-52425 Jülich Germany Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China

We present new parton distribution functions (PDFs) at next-to-leading order and next-to-next-to-leading order in perturbative QCD, derived from a comprehensive QCD analysis of high-precision datasets from combined HERA deep-inelastic scattering, the Tevatron, and the LHC. To improve constraints on quark flavor separation, we incorporate Drell-Yan pair production data, which provides critical sensitivity to the quark distributions. In addition, we include the latest W and Z boson production data from the CDF, D0, ATLAS, and CMS collaborations, further refining both quark and gluon distributions. Our nominal QCD fit integrates these datasets and examines the resulting impact on the PDFs and their associated uncertainties. Uncertainties in the PDFs are quantified using the Hessian method, ensuring robust error estimates. Furthermore, we explore the sensitivity of the strong coupling constant, αs(MZ2), and proton PDFs in light of the projected measurements from the Electron-Ion Collider, where improvements in precision are expected. The analysis also investigates the effects of inclusive jet and dijet production data, which provide enhanced constraints on the gluon PDF and αs(MZ2).

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Key Techniques on Unified Identity Authentication in OpenMBEE Integration 4

Key Techniques on Unified Identity Authentication in OpenMBE...

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4th IEEE International Conference on Electronics and Communication Engineering, ICECE 2021

作者： Xue, Junjie Zhou, Junhua Shi, Guoqiang Feng, Chaoqun Liu, Penghua Quan, Hongyan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China Institute of Electronic System Engineering Beijing China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781728194226

In order to make full use of the concept of model-based systems engineering (MBSE) and the collaborative design capability of the open model-based engineering environment (OpenMBEE) in establishing complex product development platform, we propose an effective method to solve the problem of unified identity authentication in OpenMBEE integration. The proposed method is based on the Model View Controller(MVC) architecture in OpenMBEE, which has the characteristics strong plasticity and can be benefit for expanding the function in the controller layer. In our research, we make full use of the Software Development Kit (SDK) of OpenMBEE to expand the existing functions in the controller layer, and increase the function of communicating with the application development platform, then realize the function of sharing user authentication information between the development platform and OpenMBEE. After expansion, the system includes three modules, including the Front-end Service Module(FSM), MQ based Information Receiving Service Module(IFRSM), and the OpenMBEE Back-end Server Module(BSM). The effectiveness of the proposed strategies is verified by some practical instances, which verifies that our study can provide an effective design idea for the identity authentication in OpenMBEE integration. ©2021 IEEE

关键词： Integration

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Deep Hierarchical Attention Flow for Visual Commonsense Reasoning 9th

Deep Hierarchical Attention Flow for Visual Commonsense Reas...

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Song, Yuansheng Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Bejing China

ISBN: (纸本)9783030604493

Visual Commonsense Reasoning (VCR) requires a thoroughly understanding general information connecting language and vision, as well as the background world knowledge. In this paper, we introduce a novel yet powerful deep hierarchical attention flow framework, which takes full advantage of text information in the query and candidate responses to perform reasoning over the image. Moreover, inspired by the success of machine reading comprehension, we also model the correlation among candidate responses to obtain better response representations. Extensive quantitative and qualitative experiments are conducted to evaluate the proposed model. Empirical results on the benchmark VCR1.0 show that the proposed model outperforms existing strong baselines, which demonstrates the effectiveness of our method. © 2020, Springer Nature Switzerland AG.

关键词： Visual languages

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