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A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

computers, Materials & Continua 2024年第11期81卷 3351-3369页

作者： Qi Wang Chenxin Li Chichen Lin Weijian Fan Shuang Feng Yuanzhong Wang School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China School of Data Science and Intelligent Media Communication University of ChinaBeijing100024China Beijing 797 Audio Co. Ltd.Beijing100016China

News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news *** previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different *** paper proposes a novel news media bias and factuality profiling framework assisted by correlated *** framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling *** approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and *** method improves the effectiveness of news media *** results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other *** paper further analyzes and discusses based on the experimental results.

关键词： News media profiling factuality bias correlated features

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Stochastic Block Models for Complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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The kth Power Expectile Estimation and Testing

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Communications in Mathematics and Statistics 2024年第4期12卷 573-615页

作者： Fuming Lin Yingying Jiang Yong Zhou College of Mathematics and Statistics Sichuan University of Science and EngineeringZigongSichuanPeople’s Republic of China South Sichuan Center for Applied Mathematics ZigongSichuanPeople’s Republic of China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOEand Academy of Statistics and Interdisciplinary Sciences and School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China

This paper develops the theory of the kth power expectile estimation and considers its relevant hypothesis tests for coefficients of linear regression *** prove that the asymptotic covariance matrix of kth power expectile regression converges to that of quantile regression as k converges to one and hence promise a moment estimator of asymptotic matrix of quantile *** kth power expectile regression is then utilized to test for homoskedasticity and conditional symmetry of the *** comparisons of the local power among the kth power expectile regression tests,the quantile regression test,and the expectile regression test have been *** the underlying distribution is not standard normal,results show that the optimal k are often larger than 1 and smaller than 2,which suggests the general kth power expectile regression is ***,the methods are illustrated by a real example.

关键词： The kth power expectiles Expectiles Quantiles Testing for homoskedasticity Testing for conditional symmetry Estimating asymptotic matrix of quantile regression

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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Negation Triplet Extraction with Syntactic Dependency and Semantic Consistency

arXiv

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arXiv 2024年

作者： Shi, Yuchen Yang, Deqing Liu, Jingping Xiao, Yanghua Wang, Zongyu Xu, Huimin School of Data Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China East China University of Science and Technology Shanghai China Meituan China

Previous works of negation understanding mainly focus on negation cue detection and scope resolution, without identifying negation subject which is also significant to the downstream tasks. In this paper, we propose a new negation triplet extraction (NTE) task which aims to extract negation subject along with negation cue and scope. To achieve NTE, we devise a novel Syntax&Semantic-Enhanced Negation Extraction model, namely SSENE, which is built based on a generative pretrained language model (PLM) of Encoder-Decoder architecture with a multi-task learning framework. Specifically, the given sentence’s syntactic dependency tree is incorporated into the PLM’s encoder to discover the correlations between the negation subject, cue and scope. Moreover, the semantic consistency between the sentence and the extracted triplet is ensured by an auxiliary task learning. Furthermore, we have constructed a high-quality Chinese dataset NegComment based on the users’ reviews from the real-world platform of Meituan, upon which our evaluations show that SSENE achieves the best NTE performance compared to the baselines. Our ablation and case studies also demonstrate that incorporating the syntactic information helps the PLM’s recognize the distant dependency between the subject and cue, and the auxiliary task learning is helpful to extract the negation triplets with more semantic consistency. We further demonstrate that SSENE is also competitive on the traditional CDSR task. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

arXiv

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arXiv 2023年

作者： Zhang, Jingdan Wang, Jiaan Wang, Xiaodan Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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In-beam gamma rays of CSNS Back-n characterized by black resonance filter

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Nuclear science and Techniques 2024年第10期35卷 149-160页

作者： Jin-Cheng Wang Jie Ren Wei Jiang Xi-Chao Ruan Ying-Yi Liu Hao-Lan Yang Kuo-Zhi Xu Xin-Yi Pan Qi Sun Jie Bao Han-Xiong Huang Hao-Fan Bai Jiang-Bo Bai Ping Cao Qi-Ping Chen Yong-Hao Chen Wen-Hao Duan An-Chuan Fan Rui-Rui Fan Chang-Qing Feng Min-Hao Gu Chang-Cai Han Zi-Jie Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Zhi-Jie Jiang Ling Kang Chang-Lin Lan Bo Li Feng Li Qiang Li Xiao Li Yang Li Jie Liu Rong Liu Shu-Bin Liu Yi-Na Liu Guang-Yuan Luan Chang-Jun Ning Yi-Jia Qiu Wen-Kai Ren Zhi-Zhou Ren Zhao-Hui Song Kang Sun Zhi-Xin Tan Jing-Yu Tang Sheng-Da Tang Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Zhong-Wei Wen Xiao-Guang Wu Xuan Wu Ze-Peng Wu Cong Xia Li-Kun Xie Han Yi Tao Yu Yong-Ji Yu Guo-Hui Zhang Hang-Chang Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Mao-Yuan Zhao Zhi-Hao Zhou Ke-Jun Zhu Chong Zou Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Modern Physics Fudan UniversityShanghai 200433China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China USTC Archaeometry Lab University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230027China

The back-streaming white-neutron beamline(Back-n)of the China Spallation Neutron Source is an essential neutronresearch platform built for the study of nuclear data,neutron physics,and neutron *** types of cross-sectional neutron-reaction measurements have been performed at Back-n since early *** measurements have shown that a significant number of gamma rays can be transmitted to the experimental stations of Back-n along with the neutron *** gamma rays,commonly referred to as in-beam gamma rays,can induce a non-negligible experimental background in neutron-reaction *** the characteristics of in-beam gamma rays is important for understanding the experimental ***,measuring in-beam gamma rays is challenging because most gamma-ray detectors are sensitive to neutrons;thus,discriminating between neutron-induced signals and those from in-beam gamma rays is *** this study,we propose the use of the black resonance filter method and a CeBr_(3) scintillation detector to measure the characteristics of the in-beam gamma rays of *** types of black resonance filters,^(181)Ta,^(59)Co,^(nat)Ag,and^(nat)Cd,were used in this *** time-of-flight(TOF)technique was used to select the detector signals remaining in the absorption region of the TOF spectra,which were mainly induced by in-beam gamma *** energy distribution and flux of the in-beam gamma rays of Back-n were determined by analyzing the deposited energy spectra of the CeBr_(3) scintillation detector and using Monte Carlo *** on the results of this study,the background contributions from in-beam gamma rays in neutron-reaction measurements at Back-n can be reasonably evaluated,which is beneficial for enhancing both the experimental methodology and data analysis.

关键词： In-beam gamma rays Back-n CeBr_(3)scintillator Black filter resonance technique

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