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5th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2018

作者： Chen, Yanping Wang, Han Xia, Hong Gao, Cong Wang, Zhongmin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China Shannxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (纸本)9783030037659

The effective integration of crowdsourcing answers has become research hot spots in crowdsourcing quality control. Taking into account the influence of the specialty categories of workers on the accuracy of crowdsourced answers, a crowdsourced answer integration algorithm based on the specialty categories of workers is proposed(SCAI). Firstly, SCAI use the crowdsourced answer set to determine the difficulty of the task. Secondly calculate the accuracy of each crowdsourced answer, then obtain the professional classification of the workers and update the professional accuracy. Experiments were conducted on real data sets and compared with classical majority voting method(MV) and expectation maximization evaluation algorithm(EM). The results show that the proposed algorithm can effectively improve the accuracy of crowd-sourced answer. © Springer Nature Switzerland AG 2019.

关键词： Quality control

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ESNet: Evolution and Succession network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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A Hybrid Prediction Method Based on MODWT-EMD for Time Series in Wireless Sensor networks 16

A Hybrid Prediction Method Based on MODWT-EMD for Time Serie...

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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Wang, Zhongmin Jia, Jingwen Gao, Cong Chen, Yanping Xia, Hong Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an China Xi'an University of Posts and Telecommunications School of Computer Science Technology Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9781665405539

Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid prediction methods for time series are not good, this paper proposes a hybrid prediction method based on MODWT-EMD for time series. Firstly, the original time series is decomposed into multiple frequency components by Maximal Overlapping Discrete Wavelet Transform (MODWT). Secondly, the multiple frequency components are decomposed successively by Empirical Mode Decomposition (EMD) to obtain IMF components and residuals. IMFs and residuals contain the feature information of time series. Then, the importance of the extracted feature information is scored and sorted by random forest. We select feature information with a higher score. Finally, the selected feature information is input into Bi-GRU for training. We predicted the original time series by the trained Bi-GRU and obtained the prediction results. The experimental results show that the method proposed in this paper can predict the time series effectively. © 2021 IEEE.

关键词： Empirical mode decomposition

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Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank

arXiv

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arXiv 2024年

作者： Yu, Lulu Bi, Keping Ni, Shiyu Guo, Jiafeng CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences Beijing China

Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods has primarily been validated on synthetic datasets. However, their performance on real-world click data remains unclear. Recently, Baidu released a large publicly available dataset of their web search logs. Subsequently, the NTCIR-17 ULTRE-2 task released a subset dataset extracted from it. We conduct experiments on commonly used or effective ULTR methods on this subset to determine whether they maintain their effectiveness. In this paper, we propose a Contextual Dual Learning Algorithm with Listwise Distillation (CDLA-LD) to simultaneously address both position bias and contextual bias. We utilize a listwise-input ranking model to obtain reconstructed feature vectors incorporating local contextual information and employ the Dual Learning Algorithm (DLA) method to jointly train this ranking model and a propensity model to address position bias. As this ranking model learns the interaction information within the documents list of the training set, to enhance the ranking model’s generalization ability, we additionally train a pointwise-input ranking model to learn the listwise-input ranking model’s capability for relevance judgment in a listwise manner. Extensive experiments and analysis confirm the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Bug or not bug? Labeling issue reports via user reviews for mobile apps 30

Bug or not bug? Labeling issue reports via user reviews for ...

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30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Li, Haoming Zhang, Tao Wang, Ziyuan College of Computer Science and Technology Harbin Engineering University Harbin150001 China Key Laboratory of Network Assessment Technology Institute of Information Engineering CAS Bejing100093 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)1891706446

A great number of mobile applications (apps) have been released to the market. Therefore, software maintenance for these apps become an important and challenging task. For each app, developers usually submit issue reports to report the bugs, the features, the questions, and other changes appearing in it. In the process of software maintenance, developers refer to the corresponding labels to decide which one should be fixed first. If the label of an issue report is "bug" which means the report describes a serious error, developers should fix the bug first. Otherwise, if the label is not "bug" (e.g., feature or question), developers can resolve it later. However, according to our investigation, 36.7% of issue reports in top-10 popular mobile apps are not labeled. In other words, there are not any labels in them. It is difficult for developers to decide which issue should be resolved preferentially. To resolve this problem, we propose a method to verify whether an issue report describes a bug or not by using user reviews. Developers usually extract useful information from user reviews to maintain mobile apps. In this work, we utilize tfidf, Word2Vec, and Microsoft Concept Graph (MCG) to compute the textual similarity between issue reports and user reviews related to real bug in order to find the issue reports which describe the real bugs. As a result, our approach with Word2Vec performs the best among three similarity metrics. © 2018 Universitat zu Koln. All rights reserved.

关键词： Computer software maintenance

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Social evolutionary games

Social evolutionary games

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International Conference on Game Theory for networks, GameNets

作者： Jianye Yu Yuanzhuo Wang Xiao!ong Jin Xueqi Cheng CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

In this paper, we introduce a framework of social evolutionary games (SEG) for investigating the evolution of social networks. In a SEG, a coevolutionary mechanism is adopted by agents who aim to improve his short-term utility and long-term reputation. Two examples are presented to demonstrate SEG, prisoner's dilemma game for pairwise interaction and public goods game for group interaction. Numerical simulations are performed on the two examples with different parameter settings, and the results indicates that SEG can be used as a metaphor for investigating the evolution of social networks in some scenarios.

关键词： Microscopy

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RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation

arXiv

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arXiv 2023年

作者： Fan, Run-Ze Fan, Yixing Chen, Jiangui Guo, Jiafeng Zhang, Ruqing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences Beijing China

Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendation faces challenges in the comprehensive difficulty of newly posted tweets in response to new topics, and the accurate identification of mainstream hashtags beyond semantic correctness. However, previous retrieval-based methods based on a fixed predefined mainstream hashtag list excel in producing mainstream hashtags, but fail to understand the constant flow of up-to-date information. Conversely, generation-based methods demonstrate a superior ability to comprehend newly posted tweets, but their capacity is constrained to identifying mainstream hashtags without additional features. Inspired by the recent success of the retrieval-augmented technique, in this work, we attempt to adopt this framework to combine the advantages of both approaches. Meantime, with the help of the generator component, we could rethink how to further improve the quality of the retriever component at a low cost. Therefore, we propose RetrIeval-augmented Generative Mainstream HashTag Recommender (RIGHT), which consists of three components: (i) a retriever seeks relevant hashtags from the entire tweet-hashtags set;(ii) a selector enhances mainstream identification by introducing global signals;and (iii) a generator incorporates input tweets and selected hashtags to directly generate the desired hashtags. The experimental results show that our method achieves significant improvements over state-of-the-art baselines. Moreover, RIGHT can be easily integrated into large language models, improving the performance of ChatGPT by more than 10%. Code will be released at: https://***/ict-bigdatalab/RIGHT. © 2023, CC BY.

关键词： Semantics

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Full-Duplex ISCC for Low-Altitude networks: Resource Allocation and Coordinated Beamforming

arXiv

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arXiv 2025年

作者： Chen, Yiyang Liu, Wenchao Zhang, Xuhui Ren, Jinke Xing, Huijun Wang, Shuqiang Shen, Yanyan Southern University of Science and Technology Guangdong518055 China Guangdong518055 China Shenzhen Future Network of Intelligence Institute the School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom SIAT CAS Guangdong518055 China Shenzhen University of Advanced Technology Guangdong518055 China

This paper investigates an integrated sensing, communication, and computing system deployed over low-altitude networks for enabling applications within the low-altitude economy. In the considered system, a full-duplex enabled unmanned aerial vehicle (UAV) is dispatched in the airspace, functioning as a UAV-enabled low-altitude platform (ULAP). The ULAP is capable of achieving simultaneous information transmission, target sensing, and mobile edge computing services. To reduce the overall energy consumption of the system while meeting the sensing beampattern threshold and user computation requirements, we formulate an energy consumption minimization problem by jointly optimizing the task allocation, computation resource allocation, transmit beamforming, and receive beamforming. Since the problem is non-convex and involves highly coupled variables, we propose an efficient algorithm based on alternation optimization, which decomposes the original problem into tractable convex subproblems. Moreover, we analyze the convergence and complexity of the proposed algorithm. Numerical results demonstrate that the proposed scheme saves up to 54.12% energy consumption performance compared to the benchmark schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Signaling

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Study of the response of 10B-doped MCP to wide-energy range neutrons from eV to MeV

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Nuclear science and Techniques 2024年第8期35卷 132-141页

作者： Qiang Li Li‑Jiao Wang Xiang‑Biao Qiu Jing‑Wen Li Wei Xu Tao Li Ze‑Bin Lin Chang‑Jun Ning Yong‑Hao Chen Rui‑Rui Fan Kang Sun Jing‑Yu Tang Rong Zhang Han‑Tao Jing Bo Mei Qi An Hao‑Fan Bai Jiang‑Bo Bai Jie Bao Ping Cao Qi‑Ping Chen Zhen Chen Zeng‑Qi Cui An‑Chuan Fan Chang‑Qing Feng Fan‑Zhen Feng Ke‑Qing Gao Min‑Hao Gu Chang‑Cai Han Zi‑Jie Han Guo‑Zhu He Yong‑Cheng He Yang Hong Yi‑Wei Hu Han‑Xiong Huang Wei‑Hua Jia Hao‑Yu Jiang Wei Jiang Zhi‑Jie Jiang Zheng‑Yao Jin Ling Kang Bo Li Chao Li Gong Li Jia‑Wen Li Xiao Li Yang Li Jie Liu Rong Liu Shu‑Bin Liu Guang‑Yuan Luan Bin‑Bin Qi Jie Ren Zhi‑Zhou Ren Xi‑Chao Ruan Zhao‑Hui Song Zhi‑Xin Tan Sheng‑Da Tang Peng‑Cheng Wang Zhao‑Hui Wang Zhong‑Wei Wen Xiao‑Guang Wu Xuan Wu Li‑Kun Xie Yi‑Wei Yang Han Yi Yong‑Ji Yu Guo‑Hui Zhang Lin‑Hao Zhang Mo‑Han Zhang Qi‑Wei Zhang Xian‑Peng Zhang Yu‑Liang Zhang Yue Zhang Zhi‑Yong Zhang Mao‑Yuan Zhao Lu‑Ping Zhou Zhi‑Hao Zhou Ke‑Jun Zhu Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Particle Detection and Electronics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Dongguan Songshan Lake Future School Dongguan 523808China North Night Vision Science&Technology(Nanjing)Research Institute Co.Ltd. Nanjing 211106China Sino‑French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China USTC Archaeometry Lab University of Science and Technology of ChinaHefei 230026China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China

Neutron-sensitive microchannel plates(nMCPs)have applications in neutron detection,including energy spectrum measurements,neutron-induced cross sections,and neutron imaging.10B-doped MCPs(B-MCPs)have attracted significant attention owing to their potential for exhibiting a high neutron detection efficiency over a large neutron energy *** spatial and temporal resolutions are useful for neutron energy-resolved ***,their practical applications still face many technical *** this study,a B-MCP with 10 mol%10B was tested for its response to wide-energy neutrons from eV to MeV at the Back-n white neutron source at the China Spallation Neutron *** neutron detection efficiency was calibrated at 1 eV,which is approximately 300 times that of an ordinary MCP and indicates the success of 10 B *** factors that caused the reduction in the detection efficiency were simulated and *** neutron energy spectrum obtained using B-MCP was compared with that obtained by other measurement methods,and showed very good consistency for neutron energies below tens of *** response is more complicated at higher neutron energy,at which point the elastic and nonelastic reactions of all nuclides of B-MCP gradually become *** is beneficial for the detection of neutrons,as it compensates for the detection efficiency of B-MCP for high-energy neutrons.

关键词： Neutron radiation image 10B-doped MCP Neutron response Wide-energy range neutrons

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