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A Scalable Algorithm for Fair Influence Maximization with Unbiased Estimator

IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 3881-3895页

作者： Rui, Xiaobin Wang, Zhixiao Peng, Hao Chen, Wei Yu, Philip S. China University of Mining and Technology School of Computer Science Jiangsu Xuzhou221116 China Mine Digitization Engineering Research Center of the Ministry of Education Jiangsu Xuzhou221116 China Beihang University School of Cyber Science and Technology Beijing100191 China Theory Center of Microsoft Research Asia Beijing100080 China University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

This paper studies the fair influence maximization problem with efficient algorithms. In particular, given a graph G, a community structure C consisting of disjoint communities, and a budget k, the problem asks to select a seed set S (||S|| = k) that maximizes the influence spread while narrowing the influence gap between different communities. This problem derives from some significant social scenarios, such as health interventions (e.g. suicide/HIV prevention) where individuals from racial minorities or LGBTQ communities may be disproportionately excluded from the benefits of the intervention. To depict the concept of fairness in the context of influence maximization, researchers have proposed various notions of fairness, where the welfare fairness notion that better balances fairness level and influence spread has shown promising effectiveness. However, the lack of efficient algorithms for optimizing the objective function under welfare fairness restricts its application to networks of only a few hundred nodes. In this paper, we modify the objective function of welfare fairness to maximize the exponentially weighted sum and the logarithmically weighted sum over all communities' influenced fractions (utility). To achieve efficient algorithms with theoretical guarantees, we first introduce two unbiased estimators: one for the fractional power of the arithmetic mean and the other for the logarithm of the arithmetic mean. Then, by adapting the Reverse Influence Sampling (RIS) approach, we convert the optimization problem to a weighted maximum coverage problem. We also analyze the number of reverse reachable sets needed to approximate the fair influence at a high probability. Finally, we present an efficient algorithm that guarantees 1 - 1/e - ϵ (positive objective function) or 1 + 1/e + ϵ (negative objective function) approximation for any small ϵ > 0. Experiments demonstrate that our proposed algorithm could efficiently handle large-scale networks with good performa

关键词： Approximation algorithms

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Data-driven approaches to prediction of inlet NOx concentrations in denitrification systems 23

Data-driven approaches to prediction of inlet NOx concentrat...

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6th International Conference on Big Data Technologies, ICBDT 2023

作者： Liu, Xiaoling Wang, Gang Zhang, Benqiang Sun, Yanzhen Wang, Guangbo Chen, Yu College of Computer Science & Technology Qingdao University Qingdao China Department of Science Technology and Digitization Huadian Qingdao Power Corporation Ltd Qingdao China Qingdao Hongjin Intelligent Energy Qingdao China

ISBN: (纸本)9798400707667

In order to optimize the nonlinear and hysteresis control issues in the SCR denitration system, ensure a stable nitrogen oxide concentration at the system outlet, and reduce ammonia injection operating costs effectively, this study investigates the development of an optimized prediction model using a data-driven approach. Regression prediction tests are conducted on four data-driven machine learning models: random forest, SVR, BP-NN, and LSTM-NN. After identifying the optimal model parameters, time series prediction tests for nitrogen oxide concentration at the system inlet are implemented in industrial application scenarios. Ultimately, SVR and LSTM-NN models are selected as the primary predictive control models. The anticipated future optimization and improvement aim for accurate prediction of inlet nitrogen oxide concentration, precise control of ammonia injection denitration, and consistent achievement of outlet concentration standards. © 2023 ACM.

关键词： Forecasting

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Prediction for the Load of Thermal Power Unit Based on Temporal Convolutional Network 22

Prediction for the Load of Thermal Power Unit Based on Tempo...

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5th International Conference on Big Data Technologies, ICBDT 2022

作者： Yan, Ziyi Wang, Gang Zhang, Benqiang Zhang, Lin Wang, Lei Yu, Zhongqing Chen, Yu College of Computer Science Technology Qingdao University China Department of Science Technology and Digitization Huadian Qingdao Power CORPORATION LTD. China Department of Science Technology and Digitization Cccc Mechanical and Electrical Engineering CO. LTD. China College of Computer Science Technology Qingdao University Qingdao Hongjin Intelligent Energy Co. LTD. China

ISBN: (纸本)9781450396875

Based on historical time series data of a 300MW coal-fired thermal unit in Qingdao, China, this paper proposes a GCTCN prediction model to address the problems of non-linearity, multi-factor and non-stationary state exhibited by unit load prediction. The model combines gray correlation model and temporal convolution model to predict the load for a future period, enabling the thermal unit to respond more quickly to load commands from dispatch. © 2022 ACM.

关键词： Forecasting

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Insight into microstructure evolution on anti-corrosion property of Al_(x)CoCrFeNiC_(0.01) high-entropy alloys using scanning vibration electrode technique

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Rare Metals 2023年第10期42卷 3455-3467页

作者： Bo-Kai Liao Zhan-Xiang Liang Zhi-Gang Luo Yue Liu Hao-Wei Deng Tao Zhang Xing-Peng Guo Qi-Sen Ren Hong-En Ge School of Chemistry and Chemical Engineering Guangzhou UniversityGuangzhou510006China School of Physics and Materials Science Center of Advanced Functional MaterialsGuangzhou UniversityGuangzhou510006China Joint Institute of Guangzhou University and Institute of Corrosion Science and Technology Guangzhou UniversityGuangzhou510006China Science and Technology Digitization Department China General Nuclear Power CorporationShenzhen518038China Institute of Nuclear Fuel and Materials China Nuclear Power Technology Research InstituteShenzhen518031China Hubei Key Laboratory of Material Chemistry and Service Failure School of Chemistry and Chemical EngineeringHuazhong University of Science and TechnologyWuhan430074China

The effect of micro structure on the corrosion resistance of Al_(x)CoCrFeNiC_(0.01)(x=0.2,0.7,and 1.2)high-entropy alloys(HEAs) was systematically studied in this *** microstructure evolution by regulating the Al content was analyzed in *** behavior was in situ monitored using the scanning vibration electrode technique,as well as some traditional electrochemical *** is interesting to find that the compositions of body-centered cubic(bcc) and face-centered cubic(fcc)phases changed with the rising Al content,while the corresponding electrochemical responses for both phases were discriminated using the scanning Kelvin probe force microscopy *** element was mainly distributed in the bcc phase for Al0.2(x=0.2) alloy,while its distribution changed to the fcc phase for the A10.7 and *** micro-galvanic corrosion cells formed between Cr-depleted and Cr-rich phases,resulting in the localized corrosion behaviors for the Al_(x)CoCrFeNiC_(0.01) HEAs,and the order for anti-corrosion property was Al0.2>Al1.2> Al0.7 HEAs.

关键词： High-entropy alloy Corrosion Mechanical property Scanning vibration electrode technique

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Study on the Morphological Characteristics of Multiple-Reignition Secondary Arcs on EHV/UHV Transmission Lines

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IEEE Transactions on Plasma science 2025年第4期53卷 751-759页

作者： Li, Runchang Liu, Luyao Hou, Weihong Liu, Hongshun Zhang, Kui Xu, Dongjie Shi, Yuchao Dai, Shiqiang State Grid Hangzhou Power Supply Company Substation Overhaul Center Hangzhou310015 China Shandong University School of Electrical Engineering Jinan250061 China State Grid Zhejiang Electric Power Company Ltd. Department of Equipment Hangzhou310063 China State Grid Hangzhou Power Supply Company Department of Science and Technology Digitization Hangzhou310016 China

Transient faults on extra-high-voltage/ultrahigh-voltage (EHV/UHV) transmission lines are mostly single-phase-to-ground faults, and the corresponding extinction characteristics of secondary arc affect the success rate of single-phase auto-reclosing (SPAR) and the transient stability of power system. In this article, based on the equivalent experiments performed in the laboratory, the binary image of the secondary arc is extracted by the method of grayscale expansion after the logarithmic transformation of the arc images, and the morphological characteristics of the secondary arcs are described by calculating and analyzing the skeleton and the sinuosity of the arc, the barycenter and Feret’s maximum and minimum diameters and their maximum and minimum angles. The sinuosity and the curvature distribution of the secondary arc, the barycenter velocity, momentum, acceleration, and resultant force of the secondary arc are visually plotted and quantitatively calculated. Their corresponding variation rules are also summarized. The results of this study will provide a more complete experimental basis and a better understanding of the morphological characteristics of secondary arc. © 1973-2012 IEEE.

关键词： Transient stability

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AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Chowdhury, Deepraj Das, Rupanjan Rana, Risav Dwivedi, Ashutosh Dhar Chatterjee, Pushpita Mukkamala, Raghava Rao International Institute of Information Technology Department of Electronics and Communication Naya Raipur India Copenhagen Business School Centre for Business Data Analytics Department of Digitization Frederiksberg Denmark Tennessee State University Department of Computer Science Nashville United States

ISBN: (纸本)9781665459754

With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown. © 2022 IEEE.

关键词： 5G mobile communication systems

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Design and application of new distributed photovoltaic anti-islanding protection device 6

Design and application of new distributed photovoltaic anti-...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Liu, Shengzuo Ji, Shuang Yao, Hongyan Xian, Yingnan Wang, Lei Tao, Liang Yuan, Fengjun State Grid Siping power supply company Science and technology digitization department Jilin Siping China Substation secondary maintenance center State Grid Siping power supply company Jilin Siping China Electric power equipment manufacturing installation Co. Ltd State Grid Siping power supply company Jilin Siping China

ISBN: (纸本)9798350377798

In recent years, the proportion of investment in distributed photovoltaic power generation has been increasing. However, when grid-connected power generation systems experience islanding effects, the quality of power supply to the entire power network and the personal safety of grid staff can be severely compromised. To address this issue, this paper proposes a new type of distributed photovoltaic anti-islanding protection device. The device is characterized by its simplicity and ease of implementation, reliable operation, and low cost. It eliminates the potential pitfalls associated with 'islanding operation' and resolves the 'island' risk that has plagued grid systems for years. © 2024 IEEE.

关键词： anti-islanding protection distributed photovoltaic power generation islanding protection device

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Facial Action Unit Recognition with Micro-Action-Aware Transformer 2nd

Facial Action Unit Recognition with Micro-Action-Aware Tran...

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2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Yuan, Yichen Cheng, Yifan Shao, Zhiwen Dang, Qianwen Chen, Rui Fu, Mingjian Jiang, Shengtian Li, Chunyu Ma, Lizhuang School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819650835

Facial action unit (AU) recognition is a challenging task, due to the subtlety of each AU and the correlations among AUs in global face. However, the learning of local-global features has not been thoroughly exploited in most of the existing methods. In this paper, we propose a novel micro-action-aware transformer to integrate local and global feature extractions, which effectively captures subtle AU details while maintaining the global relational modeling capacity of transformers. Besides, we jointly train facial AU recognition and facial landmark detection, in which the two correlated tasks contribute to each other and further facilitate the learning of local-global AU-related feature. Extensive experiments demonstrate that our approach achieves comparable performance to the state-of-the-art AU recognition methods on the challenging BP4D and GFT benchmarks, and works well for landmark detection. Particularly, our approach achieves average F1 score results of 63.3% and 55.8% on BP4D and GFT datasets, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： facial action unit recognition facial landmark detection Micro-action-aware transformer

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Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample

arXiv

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arXiv 2024年

作者： Shao, Zhiwen Zhu, Hancheng Zhou, Yong Xiang, Xiang Liu, Bing Yao, Rui Ma, Lizhuang School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Ministry of Education Wuhan430074 China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou221116 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal inference have been introduced to AU detection. However, most existing methods directly learn self-attention guided by AU detection, or employ common patterns for all AUs during causal intervention. The former often captures irrelevant information in a global range, and the latter ignores the specific causal characteristic of each AU. In this paper, we propose a novel AU detection framework called AC2D by adaptively constraining self-attention weight distribution and causally deconfounding the sample confounder. Specifically, we explore the mechanism of self-attention weight distribution, in which the self-attention weight distribution of each AU is regarded as spatial distribution and is adaptively learned under the constraint of location-predefined attention and the guidance of AU detection. Moreover, we propose a causal intervention module for each AU, in which the bias caused by training samples and the interference from irrelevant AUs are both suppressed. Extensive experiments show that our method achieves competitive performance compared to state-of-the-art AU detection approaches on challenging benchmarks, including BP4D, DISFA, GFT, and BP4D+ in constrained scenarios and Aff-Wild2 in unconstrained scenarios. The code is available at https://***/ZhiwenShao/AC2D. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Correction to: Uncertainty-corrected fractional generalized Pareto motion for lithium-ion battery life prediction and value-at-risk-based maintenance framework (Nonlinear Dynamics, (2025), 10.1007/s11071-024-10815-4)

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Nonlinear Dynamics 2025年第12期113卷 14963-14963页

作者： Wang, Zhen Chen, Jianxue Gao, Yan Song, Wanqing Karimi, Hamid Reza Zhang, Yujin Qi, Deyu School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Minnan University of Science and Technology Fujian Quanzhou362700 China Department of Mechanical Engineering Politecnico Di Milano Via La Masa 1 Milan20156 Italy Institute of Digitization Science and Technology South China Business College Guangdong University of Foreign Studies Guangzhou510545 China

The original article was published without its funding notes: This research was funded by the Major Project of science and technology of Quanzhou (Grant no. 2022GZ8), the technology Innovation Project of Minnan University of science and technology (Grant No. 23XTD113), Industry-University-Research Innovation Fund of the Chinese Ministry of Education (2021ZYB01003) and Education department of Guangdong Province (2022GCZX013). The original article has been updated to include the note. The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

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