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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

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Defence Technology（防务技术） 2022年第12期18卷 2150-2159页

作者： Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China School of Management Science and Engineering Anhui University of Finance and EconomicsBengbuAnhui230030China School of Computer and Communication Lanzhou University of TechnologyLanzhouGansu730050China

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,***,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of ***,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature ***,the events knowledge graph is constructed by means of sparse representation and l1 *** experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding *** experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

关键词： Text event mining Knowledge graph of events Mutual information among neighbor domains Sparse representation

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Hanyuan Zhang, Xingyu Jiang, Qize Zheng, Baihua Sun, Zhenbang Sun, Weiwei Wang, Changhu School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Institute of Intelligent Electronics and Systems China Singapore Management University Singapore ByteDance AI Lab Beijing China

ISBN: (纸本)9780999241165

Trajectory similarity computation is a core problem in the field of trajectory data queries. However, the high time complexity of calculating the trajectory similarity has always been a bottleneck in real-world applications. Learning-based methods can map trajectories into a uniform embedding space to calculate the similarity of two trajectories with embeddings in constant time. In this paper, we propose a novel trajectory representation learning framework Traj2SimVec that performs scalable and robust trajectory similarity computation. We use a simple and fast trajectory simplification and indexing approach to obtain triplet training samples efficiently. We make the framework more robust via taking full use of the sub-trajectory similarity information as auxiliary supervision. Furthermore, the framework supports the point matching query by modeling the optimal matching relationship of trajectory points under different distance metrics. The comprehensive experiments on real-world datasets demonstrate that our model substantially outperforms all existing approaches. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Trajectories

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Multi-criteria consensus sorting model with flexible linguistic preferences based on fuzzy information granulation from the perspective of preference disaggregation

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Engineering Applications of Artificial Intelligence 2025年 154卷

作者： Zhang, Shiji Zhang, Shitao Tian, Hao Deveci, Muhammet Liu, Xiaodi School of Microelectronics and Data Science Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Ma'anshan243032 China Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan

Multi-criteria group sorting (MCGS) that considers linguistic preferences involves multiple individuals evaluating alternatives and assigning them to pre-determined ordered categories based on specific criteria. Nevertheless, due to the limited availability of class information and the constrained cognitive capacity of decision-makers (DMs), it becomes challenging for DMs to furnish explicit preference information to reach consensus. Besides, decision parameters such as consensus threshold and class thresholds are also too hard to be assumed in advance. Therefore, this paper studies the preference disaggregation problem in MCGS, in which DMs are allowed to provide their pairwise comparisons in flexible linguistic expressions (FLEs) as preference information. To more fully utilize the preferences, semantic granulation is introduced. First, an individual consistency recognition model is proposed to identify inconsistent preferences and provide modification directions to the corresponding individuals. Next, semantic granulation and maximum entropy are combined into a granulation-driven information transformation model to convert the preference information based on FLEs into triangular fuzzy numbers (TFNs). Subsequently, in the consensus-driven preference disaggregation model, decision parameters and sorting results can be obtained at the premise of consensus by adjusting weights. Ultimately, to substantiate the effectiveness of the proposal, two numerical applications concerning the sorting of government venture capitals and information system suppliers are presented, along with comparative analysis, sensitivity analysis, and simulation analysis. © 2025 The Authors

关键词： Information granules

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Learning Unified Embeddings for Recommendation via Meta-path Semantics 21

Learning Unified Embeddings for Recommendation via Meta-path...

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29th ACM International Conference on Multimedia, MM 2021

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming 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 Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781450386517

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance;2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Embeddings

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Downscaling multilayer soil moisture using parameterized depth profiles associated with environmental factors

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Journal of Hydrology 2025年

作者： Mo Zhang Yong Ge Yuxin Ma Yan Jin Yingying Chen Shaomin Liu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang 330022 China Key Laboratory Of Intelligent Monitoring and Comprehensive Management Of Watershed Ecology Nanchang 330022 China New South Wales Department of Climate Change Energy the Environment and Water Parramatta NSW 2150 Australia School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing 210023 China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing 100101 China State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical Science Beijing Normal University Beijing 100875 China

High-resolution multilayer soil moisture is valuable for water resources management, drought monitoring, and crop optimization. However, integrating process-based models into statistical downscaling methods to estimate multilayer soil moisture can increase both prediction uncertainty and computational complexity of parameterization. Here, three parameterized equations (Peak, Trough, and Balance) were proposed to quantify soil moisture profiles associated with environmental factors, and downscale multilayer soil moisture. Global (0–40 cm) and local (0–10, 10–20, and 20–40 cm) fitting strategies represented the vertical extent of environmental influences. Three machine learning models were employed to downscale satellite soil moisture and predict depth parameters, which were then used to generate sub-surface soil moisture. The results indicated that the local fitting strategy consistently outperformed the global strategy across all depths, reducing the mean absolute error (MAE) and root mean square error (RMSE) by 81.8 % and 80.4 %, and increasing the coefficient of determination (R 2 ) by 22.5 %. Furthermore, local fitting revealed distinct nonlinear and linear patterns within the 0–20 cm and 20–40 cm intervals, respectively. The prediction accuracy of sub-surface soil moisture was improved compared to surface results, with MAE and RMSE reduced by 30.2 %–60.7 % and 34.9 %–56.7 %, respectively. The modified local fitting strategy further enhanced prediction performance, with the Peak–Trough–Balance combination recommended as the optimal configuration. Future studies should account for variations in soil and vegetation cover, refine interval division by incorporating soil-forming processes, and adopt advanced uncertainty quantification methods to enhance its adaptability and robustness. This study provides a reference for multilayer soil moisture downscaling in large-scale regions with depth variability and environmental heterogeneity.

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Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery

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Cyborg and Bionic Systems 2023年第1期4卷 121-135页

作者： Ling Li Xiaojian Li Bo Ouyang Hangjie Mo Hongliang Ren Shanlin Yang School of Management Hefei University of TechnologyHefeiChina Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) Hefei University of TechnologyHefeiChina Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance(Ministry of Education) Hefei University of TechnologyHefeiChina Department of Biomedical Engineering Faculty of EngineeringNational University of SingaporeSingaporeSingapore Department of Electronic Engineering Shun Hing Institute of Advanced EngineeringThe Chinese University of Hong KongHong KongChina National Engineering Laboratory for Big Data Distribution and Exchange Technologies ShanghaiChina

In the robot-assisted minimally invasive surgery,if a collision occurs,the robot system program could be damaged,and normal tissues could be *** avoid collisions during surgery,a 3-dimensional collision avoidance method is proposed in this *** proposed method is predicated on the design of 3 strategic vectors:the collision-with-instrument-avoidance(CI)vector,the collision-with-tissues-avoidance(CT)vector,and the constrained-control(CC)*** CI vector demarcates 3 specific directions to forestall collision among the surgical *** CT vector,on the other hand,comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget ***,the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position,ensuring precision in its movements,in alignment with the surgical *** results verify the proposed collision avoidance method for robot-assisted minimally invasive *** code and data are available at https://***/cynerelee/collision-avoidance.

关键词： collision desired verify

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Top-k Dominating Queries on Incomplete data

Top-k Dominating Queries on Incomplete Data

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作者： Miao, Xiaoye Gao, Yunjun Zheng, Baihua Chen, Gang Cui, Huiyong College of Computer Science Zhejiang University 38 Zheda Road Hangzhou310027 China Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China School of Information Systems Singapore Management University Singapore178902 Singapore

The top-k dominating (TKD) query returns the k objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-k queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data, which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for answering TKD queries over incomplete data. Our methods employ some novel techniques, such as upper bound score pruning, bitmap pruning, and partial score pruning, to boost query efficiency. Extensive experimental evaluation using both real and synthetic datasets demonstrates the effectiveness of our developed pruning heuristics and the performance of our presented algorithms. © 2015 IEEE.

关键词： Decision support systems

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Research on Tourist Destination Passenger Flow Prediction Based on Decomposition Clustering Technology for data Continuity Problem

Research on Tourist Destination Passenger Flow Prediction Ba...

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2024 International Conference on Culture-Oriented Science and Technology, CoST 2024

作者： Feng, Lianze Mu, Lin Yang, Yi Li, Caihong Lanzhou University School of Information Science and Engineering Lanzhou730000 China Ministry of Culture and Tourism Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection Lanzhou730000 China Tourism Quality Supervision and Management Institute of China Beijing100740 China

ISBN: (纸本)9798350380347

Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourism data. This study addresses these challenges by proposing a multi-feature combined prediction model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Incremental Entropy-K-means clustering-Time Convolutional Network-Coati Optimization Algorithm (CEEMDAN-IEK-TT-COA). The model integrates signal decomposition, clustering reconstruction, component prediction, and optimization algorithms. key features are selected through feature engineering, followed by data decomposition and reconstruction using CEEMDAN, Incremental Entropy, and K-means clustering. A model pool of Time Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory network (LSTM), and Transformer filters optimal sub-models for combined prediction. The Coati Optimization Algorithm (COA) refines the final prediction. Experimental results on Jiuzhaigou and Siguniangshan datasets demonstrate significant RMSE improvements of 21.81 % and 17.84 %, validating the model's effectiveness. © 2024 IEEE.

关键词： Long short-term memory

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Risk factors and machine learning prediction models for bronchopulmonary dysplasia severity in the Chinese population

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World Journal of Pediatrics 2023年第6期19卷 568-576页

作者： Wen He Lan Zhang Rui Feng Wei-Han Fang Yun Cao Si-Qi Sun Peng Shi Jian-Guo Zhou Liang-Feng Tang Xiao-Bo Zhang Yuan-Yuan Qi Department of Respirology Children’s HospitalFudan University399 Wanyuan RoadShanghai 201102China Department of Neonatology Children’s HospitalFudan UniversityShanghaiChina Shanghai Key Laboratory of Intelligent Information Processing School of Management and StatisticsFudan UniversityShanghaiChina Shanghai Pinghe Bilingual School ShanghaiChina Department of Data Management and Statistics Children’s Hospital of Fudan UniversityShanghaiChina Department of Urology Children’s HospitalFudan UniversityShanghaiChina

Background Bronchopulmonary dysplasia(BPD)is a common chronic lung disease in extremely preterm *** outcome and clinical burden vary dramatically according to *** some prediction tools for BPD exist,they seldom pay attention to disease severity and are based on populations in developed *** study aimed to develop machine learning prediction models for BPD severity based on selected clinical factors in a Chinese *** In this retrospective,single-center study,we included patients with a gestational age<32 weeks who were diagnosed with BPD in our neonatal intensive care unit from 2016 to *** collected their clinical information during the maternal,birth and early postnatal *** factors were selected through univariable and ordinal logistic regression *** models based on logistic regression(LR),gradient boosting decision tree,XGBoost(XGB)and random forest(RF)models were implemented and assessed by the area under the receiver operating characteristic curve(AUC).Results We ultimately included 471 patients(279 mild,147 moderate,and 45 severe cases).On ordinal logistic regression,gestational diabetes mellitus,initial fraction of inspiration O_(2) value,invasive ventilation,acidosis,hypochloremia,C-reactive protein level,patent ductus arteriosus and Gram-negative respiratory culture were independent risk factors for BPD *** the XGB,LR and RF models(AUC=0.85,0.86 and 0.84,respectively)all had good *** We found risk factors for BPD severity in our population and developed machine learning models based on *** models have good performance and can be used to aid in predicting BPD severity in the Chinese population.

关键词： Bronchopulmonary dysplasia Machine learning-Prediction model Preterm

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