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4. Dynamic Analysis of Emergency Reset of Topaz Type Space Nuclear Reactor Safety Rod

Author

LI Jingwei, LIU Shihang, ZHANG Guanhua, YAO Chengzhi, PENG Zhaohui, GUO Zhijia

Subjects
space nuclear reactor, safety rod, dynamic analysis, Nuclear engineering. Atomic power, TK9001-9401, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The safety rod system is the actuator for reactivity control and nuclear safety protection in Topaz type space nuclear reactors. Due to the uneven distribution of reactor core temperature field during the operation of a Topaz type space nuclear reactor in outer space orbit, the thermal expansion of the safety rod guide tube inside the nuclear reactor will cause certain radial deformation. When needs to stop the reactivity of nuclear reactor, the safety rod is affected by frictional resistance caused by collision with the bent guide tube during the emergency reset process. In the rod drop study for pressurized water reactor (PWR) control rods, the guide tubes are bent radially due to seismic and thermal loads, and even the shape of guide tube become “C” or “S” under different loads. How different shapes of guide tubes affect control rod drop is a variable studied by many researchers. In space nuclear reactors, only the effect of heat load on the guide tube needs to be considered, and the safety rod cannot rely on gravity, but is ejected into the reactor through the drive mechanism. In this paper, the changes in rod drop time and frictional resistance when the radial deformation of the guide tube exceeds its minimum clearance with the safety rod were researched. The radial deformation of the guide tube caused by the temperature field calculation results was taken as the input variable of the simulation analysis. A dynamic model of the safety rod system was established based on three-dimensional multi body dynamics simulation software. By accurately simulating the friction collision process and reasonably handling the rigid flexible coupling problem, the influence of radial deformation of the guide tube on the emergency reset of the safety rod was calculated and analyzed. It can be found that when the radial deformation of the guide tube exceeds 0.16 mm, the parameter sensitivity of the studied variable increases. At the same time, the variation of friction value is more complex and the contact collision is more violent. Through the simulation analysis, the maximum bending deformation of the guide tube is determined, and the failure of the safety rod to achieve the design goal due to excessive deformation is avoided. The simulation results show that the radial deformation of guide tube is positively correlated with the rod drop time and friction resistance of safety rod, which can provide reference for the design of safety rod system.

Published
2024
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5. MHGNN: Hybrid Graph Neural Network with Mixers for Multi-interest Session-Aware Recommendation

Author

Cui, Mingyu, Peng, Zhaohui, Chu, Yaohui, Lu, Jikun, Tan, Yashu, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Tung, Anthony, editor, Zheng, Zhonglong, editor, Yang, Zhengyi, editor, Wang, Xiaoyang, editor, and Guo, Hongjie, editor

Published
2024
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6. Filter-Enhanced Multi-interest Network for Sequential Recommendation

Author

Cui, Mingyu, Peng, Zhaohui, Chu, Yaohui, Lu, Jikun, Tan, Yashu, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Tung, Anthony, editor, Zheng, Zhonglong, editor, Yang, Zhengyi, editor, Wang, Xiaoyang, editor, and Guo, Hongjie, editor

Published
2024
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7. MGCN-CT: Multi-type Vehicle Fuel Consumption Prediction Based on Module-GCN and Config-Transfer

Author

Li, Hao, Cheng, Qianwen, Peng, Zhaohui, Tan, Yashu, Chen, Zengzhe, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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8. LAF: A Local Depth Autoregressive Framework for Cardinality Estimation of Multi-attribute Queries

Author

Cheng, Qianwen, Li, Hao, Wang, Dawei, Zhang, Yue, Peng, Zhaohui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Song, Xiangyu, editor, Feng, Ruyi, editor, Chen, Yunliang, editor, Li, Jianxin, editor, and Min, Geyong, editor

Published
2024
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11. Probing the cosmic opacity from Future Gravitational Wave Standard Sirens

Author

Zhou, Lu, Fu, Xiangyun, Peng, Zhaohui, and Chen, Jun

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

In this work, using the Gaussian Process, we explore the potentiality of future gravitational wave (GW) measurement to probe cosmic opacity through comparing its opacity-free luminosity distance (LD) with the opacity-dependent one from type Ia supernovae (SNIa). GW data points are simulated from the third generation Einstein Telescope, and SNIa data are taken from the Joint Light Analysis (JLA) or Pantheon compilation. The advantages of using Gaussian Process are that one may match SNIa data with GW data at the same redshift and use all available data to probe cosmic opacity. We obtain that the error bar of the constraint on cosmic opacity can be reduced to $\sigma_{\epsilon}\sim 0.011$ and $0.006$ at $1\sigma$ confidence level (CL) for JLA and Pantheon respectively in a cosmological-independent way. Thus, the future GW measurements can give competitive results on the cosmic opacity test. Furthermore, we propose a method to probe the spatial homogeneity of the cosmic transparency through comparing the reconstructed LD from the mock GW with the reconstructed one from SNIa data in a flat $\Lambda$CDM with the Gaussian Process. The result shows that a transparent universe is favored at $1\sigma$ CL, although the best-fit value of cosmic opacity is redshift-dependent., Comment: 19pages, 3figures, accepted by PRD

Published
2019
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12. Mutual Clustering on Comparative Texts via Heterogeneous Information Networks

Author

Cao, Jianping, Wang, Senzhang, Wen, Danyan, Peng, Zhaohui, Yu, Philip S., and Wang, Fei-yue

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be ``comparative'' since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem.

Published
2019

13. Cross-Domain Recommendation for Cold-Start Users via Neighborhood Based Feature Mapping

Author

Wang, Xinghua, Peng, Zhaohui, Wang, Senzhang, Yu, Philip S., Fu, Wenjing, and Hong, Xiaoguang

Subjects
Computer Science - Information Retrieval, Computer Science - Social and Information Networks
Abstract

Collaborative Filtering (CF) is a widely adopted technique in recommender systems. Traditional CF models mainly focus on predicting a user's preference to the items in a single domain such as the movie domain or the music domain. A major challenge for such models is the data sparsity problem, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all. Although Cross-Domain Collaborative Filtering (CDCF) is proposed for effectively transferring users' rating preference across different domains, it is still difficult for existing CDCF models to tackle the cold-start users in the target domain due to the extreme data sparsity. In this paper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model for cold-start users in the target domain. Firstly, in order to better characterize users in sparse domains, we take the users' similarity relationship on rating behaviors into consideration and propose the Matrix Factorization by incorporating User Similarities (MFUS) in which three similarity measures are proposed. Next, to perform knowledge transfer across domains, we propose a neighborhood based gradient boosting trees method to learn the cross-domain user latent feature mapping function. For each cold-start user, we learn his/her feature mapping function based on the latent feature pairs of those linked users who have similar rating behaviors with the cold-start user in the auxiliary domain. And the preference of the cold-start user in the target domain can be predicted based on the mapping function and his/her latent features in the auxiliary domain. Experimental results on two real data sets extracted from Amazon transaction data demonstrate the superiority of our proposed model against other state-of-the-art methods., Comment: 16 pages, 8 figures

Published
2018

15. Dual‐Phase Enhanced CT‐Derived Radiomics Nomogram for Progression‐Free Survival Prediction in Stage IV Lung Adenocarcinoma.

Author

Sun, Haitao, Peng, Zhaohui, Chen, Guoyue, Dai, Zhengjun, Yao, Jian, and Zhou, Peng

Subjects
*COMPUTED tomography, *RADIOMICS, *DECISION making, *REGRESSION analysis, *NOMOGRAPHY (Mathematics)
Abstract

Purpose: The objective is to establish a radiomics nomogram (Rad‐nomogram) using dual‐phase enhanced computed tomography (DPE‐CT) for the prediction of progression‐free survival (PFS) in patients diagnosed with stage IV lung adenocarcinoma (ADC). Methods: From DPE‐CT scans, radiomic characteristics were retrieved from 133 patients diagnosed with stage IV lung ADC. Clinical data were analyzed using univariate and multivariate Cox regression analyses. The radiomics signature was combined with clinical features employing multivariate Cox analysis in order to develop a Rad‐nomogram. The predictive efficiency of the nomogram was evaluated using survival studies, such as Kaplan–Meier curves and Harrell's C‐index. The benefits and clinical utility of various models were compared using the net reclassification index (NRI), decision curve analysis (DCA), and integrated discrimination improvement (IDI). Results: In the test cohort, the C‐indexes for the clinical, artery, and vein phase CT models were 0.675, 0.691, and 0.678, respectively. The dual‐phase achieved a C‐index of 0.731, exceeding the CT model, while the developed nomogram reached a C‐index of 0.783. The Kaplan–Meier survival study classified patients into low‐risk and high‐risk groups related to PFS using the Rad‐nomogram (p < 0.05). The Rad‐nomogram demonstrated a greater net advantage when compared with clinical and Rad models, as indicated by positive values of the NRI and IDI (ranging from 11.6% to 52.6%, p < 0.05). Conclusion: The Rad‐nomogram, employing DPE‐CT scans, offers a promising approach to predict PFS in individuals diagnosed with stage IV lung ADC. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Horizontal-Transverse Coherence of Bottom-Received Acoustic Field in Deep Water with an Incomplete Sound Channel.

Author

Wang, Qianyu, Peng, Zhaohui, Zhang, Bo, Zhu, Feilong, Luo, Wenyu, Wang, Tongchen, Zhang, Lingshan, and Mao, Junjie

Subjects
COHERENCE (Physics), ACOUSTIC field, INHOMOGENEOUS materials, CHANNELS (Hydraulic engineering), COMPUTER simulation
Abstract

The horizontal-transverse coherence of low-frequency (300 Hz) and long-range (10–40 km) acoustic fields near the bottom in deep water is investigated based on experimental data obtained from the South China Sea. The results indicate that the horizontal-transverse coherence length exhibits a strong dependence on the source-receiver distance, with fluctuations consistent with sound intensity trends. In high-intensity regions, the horizontal-transverse coherence is relatively high, with a coherence length exceeding 600 λ, where λ is the acoustic wavelength, whereas in low-intensity regions, the horizontal-transverse coherence decreases significantly, with the coherence length shortening to 10–30 λ. The physical mechanisms underlying the horizontal-transverse coherence are analyzed using the ray theory. In high-intensity regions, the energy of the dominant ray (the ray with the highest energy) accounts for over 70% of the total energy of the rays, exerting a decisive influence on the coherence coefficient and leading to stable horizontal-transverse coherence in the received acoustic field. In contrast, in low-intensity regions, the energy distribution is dispersed, and when amplitude and phase disturbances due to spatial inhomogeneity are introduced, the horizontal coherence deteriorates significantly. The numerical simulations are also performed, and the results are consistent with the experimental observations. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Bearing-Only Multi-Target Localization Incorporating Waveguide Characteristics for Low Detection Rate Scenarios in Shallow Water.

Author

Mei, Xiaohan, Zhang, Bo, Zhai, Duo, and Peng, Zhaohui

Subjects
CROSS correlation, SIGNAL-to-noise ratio, BEAMFORMING, CONFIDENCE
Abstract

Bearing-only multi-target localization (BOMTL) determines the positions of multiple targets by intersecting bearing lines from multiple spatial locations. However, non-ideal measurements can result in a large number of ghost targets. A β-S-dimensional assignment (β-SDA) method incorporating waveguide characteristics is proposed to address the BOMTL problem in shallow water with low detection rates. The estimated distance for the warping transformation is derived from the intersection points of the bearing lines, then the autocorrelation function of the broadband beamforming output is transformed using a warping operator to obtain the corresponding characteristic spectrum. The peaks in the characteristic spectrum correspond to the cross-correlation terms of the normal modes, with the frequencies of these peaks related to the ratio of the actual distance to the estimated distance of the sound source. The global target localization results are obtained using the proposed method, which incorporates confidence coefficients derived from the characteristic spectrum and geometric intersection information from the bearing lines. Simulation and sea trial data demonstrate that the β-SDA method effectively overcomes the limitation of pure bearing-only localization in low detection rate scenarios under a given signal-to-noise ratio (SNR), and can localize target positions without requiring precise prior environmental parameters. [ABSTRACT FROM AUTHOR]

Published
2024
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18. MISS: A Multi-user Identification Network for Shared-Account Session-Aware Recommendation

Author

Wen, Xinyu, Peng, Zhaohui, Huang, Shanshan, Wang, Senzhang, Yu, Philip S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Lee, Wang-Chien, editor, Tseng, Vincent S., editor, Kalogeraki, Vana, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor

Published
2021
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19. AE-UPCP: Seeking Potential Membership Users by Audience Expansion Combining User Preference with Consumption Pattern

Author

Xu, Xiaokang, Peng, Zhaohui, Wang, Senzhang, Huang, Shanshan, Yu, Philip S., Hao, Zhenyun, Wang, Jian, Wang, Xue, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Lee, Wang-Chien, editor, Tseng, Vincent S., editor, Kalogeraki, Vana, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor

Published
2021
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20. GSimRank: A General Similarity Measure on Heterogeneous Information Network

Author

Zhang, Chuanyan, Hong, Xiaoguang, Peng, Zhaohui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Zhang, Rui, editor, Lee, Young-Koo, editor, Sun, Le, editor, and Moon, Yang-Sae, editor

Published
2020
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21. Long Short-Term Memory with Sequence Completion for Cross-Domain Sequential Recommendation

Author

Yang, Guang, Hong, Xiaoguang, Peng, Zhaohui, Xu, Yang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Zhang, Rui, editor, Lee, Young-Koo, editor, Sun, Le, editor, and Moon, Yang-Sae, editor

Published
2020
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22. Recommendation with Temporal Dynamics Based on Sequence Similarity Search

Author

Yang, Guang, Hong, Xiaoguang, Peng, Zhaohui, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Qiu, Meikang, editor

Published
2020
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23. Heterogeneous Graph Embedding for Cross-Domain Recommendation Through Adversarial Learning

Author

Li, Jin, Peng, Zhaohui, Wang, Senzhang, Xu, Xiaokang, Yu, Philip S., Hao, Zhenyun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nah, Yunmook, editor, Cui, Bin, editor, Lee, Sang-Won, editor, Yu, Jeffrey Xu, editor, Moon, Yang-Sae, editor, and Whang, Steven Euijong, editor

Published
2020
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24. Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules.

Author

Tao, Shiyou, Peng, Zhaohui, and Zheng, Weiguang

Abstract

Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Spatial Task Allocation Based on User Trajectory Prediction

Author

Jiang, Yun, He, Wei, Cui, Lizhen, Yang, Qian, Peng, Zhaohui, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Sun, Yuqing, editor, Lu, Tun, editor, Xie, Xiaolan, editor, Gao, Liping, editor, and Fan, Hongfei, editor

Published
2019
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37. Cross-Domain Recommendation for Mapping Sentiment Review Pattern

Author

Xu, Yang, Peng, Zhaohui, Hu, Yupeng, Hong, Xiaoguang, Fu, Wenjing, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Liu, Weiru, editor, Giunchiglia, Fausto, editor, and Yang, Bo, editor

Published
2018
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38. SARFM: A Sentiment-Aware Review Feature Mapping Approach for Cross-Domain Recommendation

Author

Xu, Yang, Peng, Zhaohui, Hu, Yupeng, Hong, Xiaoguang, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Hacid, Hakim, editor, Cellary, Wojciech, editor, Wang, Hua, editor, Paik, Hye-Young, editor, and Zhou, Rui, editor

Published
2018
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43. HOMMIT: A Sequential Recommendation for Modeling Interest-Transferring via High-Order Markov Model

Author

Xu, Yang, Hong, Xiaoguang, Peng, Zhaohui, Hu, Yupeng, Yang, Guang, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bouguettaya, Athman, editor, Gao, Yunjun, editor, Klimenko, Andrey, editor, Chen, Lu, editor, Zhang, Xiangliang, editor, Dzerzhinskiy, Fedor, editor, Jia, Weijia, editor, Klimenko, Stanislav V., editor, and Li, Qing, editor

Published
2017
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44. Measuring the Similarity of Nodes in Signed Social Networks with Positive and Negative Links

Author

Zhu, Tianchen, Peng, Zhaohui, Wang, Xinghua, Hong, Xiaoguang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Lei, editor, Jensen, Christian S., editor, Shahabi, Cyrus, editor, Yang, Xiaochun, editor, and Lian, Xiang, editor

Published
2017
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45. Review-Based Cross-Domain Recommendation Through Joint Tensor Factorization

Author

Song, Tianhang, Peng, Zhaohui, Wang, Senzhang, Fu, Wenjing, Hong, Xiaoguang, Yu, Philip S., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Candan, Selçuk, editor, Chen, Lei, editor, Pedersen, Torben Bach, editor, Chang, Lijun, editor, and Hua, Wen, editor

Published
2017
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50. Maximizing the Influence Ranking Under Limited Cost in Social Network

Author

Hong, Xiaoguang, Liu, Ziyan, Peng, Zhaohui, Chen, Zhiyong, Li, Hui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Feifei, editor, Shim, Kyuseok, editor, Zheng, Kai, editor, and Liu, Guanfeng, editor

Published
2016
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