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1,676 results on '"Yang, Xiaoyong"'

33. Research on the Comprehensive Value Evaluation Method of Research Institutions in Power Grid Enterprises

Author

Jiang, Xuebin, Yang, Xiaoyong, Liu, Fuyan, Ying, Qi, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Sedon, Mohd Fauzi bin, editor, Khan, Intakhab Alam, editor, BİRKÖK, Mehmet CÜNEYT, editor, and Chan, KinSun, editor

Published
2023
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34. Optimization Technology of Grid Transportation and Inspection Cost of Distribution Network Considering Complex Influence Factors

Author

Yang, Xiaoyong, Yu, Xiaoyan, Yu, Min, Liu, Fuyan, Hao, Hongzhi, Wu, Feng, Dou, Runliang, Editor-in-Chief, Liu, Jing, Editor-in-Chief, Khasawneh, Mohammad T., Editor-in-Chief, Balas, Valentina Emilia, Series Editor, Bhowmik, Debashish, Series Editor, Khan, Khalil, Series Editor, Masehian, Ellips, Series Editor, Mohammadi-Ivatloo, Behnam, Series Editor, Nayyar, Anand, Series Editor, Pamucar, Dragan, Series Editor, Shu, Dewu, Series Editor, Qiu, Daowen, editor, Jiao, Yusheng, editor, and Yeoh, William, editor

Published
2023
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42. Explore User Neighborhood for Real-time E-commerce Recommendation

Author

Xie, Xu, Sun, Fei, Yang, Xiaoyong, Yang, Zhao, Gao, Jinyang, Ou, Wenwu, and Cui, Bin

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been widely applied in industrial settings, owing to their efficiency and effectiveness. Despite their success, we argue that these approaches ignore local information hidden in similar users. To tackle this problem, user-based methods exploit similar user relations to make recommendations in a local perspective. Nevertheless, traditional user-based methods, like userKNN and matrix factorization, are intractable to be deployed in the real-time applications since such transductive models have to be recomputed or retrained with any new interaction. To overcome this challenge, we propose a framework called self-complementary collaborative filtering~(SCCF) which can make recommendations with both global and local information in real time. On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information. On the other hand, it can identify similar users for each user in real time by inferring user representations on the fly with an inductive model. The proposed framework can be seamlessly incorporated into existing inductive UI approach and benefit from user neighborhood with little additional computation. It is also the first attempt to apply user-based methods in real-time settings. The effectiveness and efficiency of SCCF are demonstrated through extensive offline experiments on four public datasets, as well as a large scale online A/B test in Taobao., Comment: To appear in ICDE 2021

Published
2021

43. Commonsense knowledge adversarial dataset that challenges ELECTRA

Author

Lin, Gongqi, Miao, Yuan, Yang, Xiaoyong, Ou, Wenwu, Cui, Lizhen, Guo, Wei, and Miao, Chunyan

Subjects
Computer Science - Computation and Language
Abstract

Commonsense knowledge is critical in human reading comprehension. While machine comprehension has made significant progress in recent years, the ability in handling commonsense knowledge remains limited. Synonyms are one of the most widely used commonsense knowledge. Constructing adversarial dataset is an important approach to find weak points of machine comprehension models and support the design of solutions. To investigate machine comprehension models' ability in handling the commonsense knowledge, we created a Question and Answer Dataset with common knowledge of Synonyms (QADS). QADS are questions generated based on SQuAD 2.0 by applying commonsense knowledge of synonyms. The synonyms are extracted from WordNet. Words often have multiple meanings and synonyms. We used an enhanced Lesk algorithm to perform word sense disambiguation to identify synonyms for the context. ELECTRA achieves the state-of-art result on the SQuAD 2.0 dataset in 2019. With scale, ELECTRA can achieve similar performance as BERT does. However, QADS shows that ELECTRA has little ability to handle commonsense knowledge of synonyms. In our experiment, ELECTRA-small can achieve 70% accuracy on SQuAD 2.0, but only 20% on QADS. ELECTRA-large did not perform much better. Its accuracy on SQuAD 2.0 is 88% but dropped significantly to 26% on QADS. In our earlier experiments, BERT, although also failed badly on QADS, was not as bad as ELECTRA. The result shows that even top-performing NLP models have little ability to handle commonsense knowledge which is essential in reading comprehension., Comment: To appear in ICARCV2020

Published
2020

44. Large Scale Product Graph Construction for Recommendation in E-commerce

Author

Yang, Xiaoyong, Zhu, Yadong, Zhang, Yi, Wang, Xiaobo, and Yuan, Quan

Subjects
Computer Science - Information Retrieval
Abstract

Building a recommendation system that serves billions of users on daily basis is a challenging problem, as the system needs to make astronomical number of predictions per second based on real-time user behaviors with O(1) time complexity. Such kind of large scale recommendation systems usually rely heavily on pre-built index of products to speedup the recommendation service so that online user waiting time is un-noticeable. One important indexing structure is the product-product index, where one can retrieval a list of ranked products given a seed product. The index can be viewed as a weighted product-product graph. In this paper, we present our novel technologies to efficiently build such kind of indexed product graphs. In particular, we propose the Swing algorithm to capture the substitute relationships between products, which can utilize the substructures of user-item click bi-partitive graph. Then we propose the Surprise algorithm for the modeling of complementary product relationships, which utilizes product category information and solves the sparsity problem of user co-purchasing graph via clustering technique. Base on these two approaches, we can build the basis product graph for recommendation in Taobao. The approaches are evaluated comprehensively with both offline and online experiments, and the results demonstrate the effectiveness and efficiency of the work.

Published
2020

45. Optimized Cu2O-{100} facet for generation of different reactive oxidative species via peroxymonosulfate activation at specific pH values to efficient acetaminophen removal

Author

Chen Ke, Yang Xudong, Hao Rui, Shao Minghui, Yang Xiaoyong, Li Xiuze, Li Yuxuan, Liu Juzhe, and Zhang Sai

Subjects
exposure facet of cu2o, generation mechanism of 1o2, wide ph range, Technology, Chemical technology, TP1-1185, Physical and theoretical chemistry, QD450-801
Abstract

Activation of peroxymonosulfate (PMS) to degrade persistent organic contaminants has received increasing attention in wastewater purification. In this study, Cu2O catalysts with different exposed crystal facets were prepared, characterized, and evaluated for acetaminophen (ACE) degradation through PMS activation. The experimental results showed that cubic Cu2O with {100} facets exhibited remarkable activity on ACE removal by PMS activation in wide pH range of 3–11. DFT calculations indicated that Cu2O-{100} displayed higher electron transfer efficiency and PMS adsorption ability, further improving PMS activation. The radicals quenching experiments and electron paramagnetic resonance (EPR) results illustrated that singlet oxygen (1O2) was dominant reactive oxidative species (ROSs) during oxidation reactions and the relevant generation pathways were distinctly elucidated. Finally, the possible PMS activation mechanisms were discussed for ACE degradation in a wide pH range. This study will provide new insights to disclose PMS-based advanced oxidation processes (AOPs), and offer a new approach for wastewater purification by non-radical reactions.

Published
2023
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46. Research on the application of area safety assessment model in coal mine safety management

Author

YANG Zheng, YANG Xiaoyong, WANG Yu, GONG Hanjun, ZHU Jie, HOU Gang, YANG Feiwen, and FANG Qian

Subjects
coal mine safety management, area security assessment, unified area division, analytic hierarchy process, entropy weighting method, multi source data fusion, Mining engineering. Metallurgy, TN1-997
Abstract

The safety evaluation granularity of the current coal mine safety monitoring and management system is basically at the mine level or subsystem level. It cannot be finely managed for different areas of the mine. In order to solve the above problem, a unified area division method is proposed for safety risk assessment based on the features of operational scenarios in various areas of the mine. This method combines information such as risks, hidden dangers, disasters with area operating conditions, equipment maintenance management, and personnel positioning to unify and organize data from various safety systems. A comprehensive assessment of the safety indicator system is conducted from four dimensions: human, machine, environment, and management. The weights of each indicator in the area safety assessment are calculated through a combination of subjective and objective weighting methods. The subjective weighting is achieved through the analytic hierarchy process, while the objective weighting method is achieved through the entropy weighting method. The method constructs an area safety assessment model, which quantitatively evaluates and classifies the current safety situation of coal mines. The method uses normalized fusion weights to calculate the basic safety score, and considers high-risk combinations, historical trend changes, and inter regional coupling effects to modify the score. The comprehensive safety scores of all levels of coal mine areas are obtained. This model has been successfully applied to the intelligent comprehensive control platform of Shaanxi Xiaobaodang Mining Co., Ltd.. It provides effective reference for accurately evaluating underground safety risks in coal mines and improving the level of coal mine safety management.

Published
2023
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