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4th International Conference on Computer Science and Application Engineering, CSAE 2020

作者： Yang, Wujuan Qian, Xiaofei School of Management and Key Laboratory of Process Optimization and Intelligent Decision-making of the Ministry of Education Hefei University of Technology Hefei China

ISBN: (纸本)9781450377720

The ubiquity of vulnerability in software products poses a serious threat to network security and user system. Patching for the software is a common tool to ease the security issue. Whereas, not all users will choose to patch due to the consideration on compatibility and inconvenience. Thus, it is significant to induce users to patch for the software. Based on the background of this practical problem, this paper proposes two models (a benchmark model and a price discount model) to analyze the user's buying and patching behavior, the pricing strategy of the software vendor, and the vendor's benefits. By comparing the results of the benchmark model and the price discount model, we find that the price discount policy will result in a higher group security level and more benefits for the vendor. © 2020 ACM.

关键词： Game theory

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A Credit management Mechanism for Peer-to-Peer Electricity Trading in Blockchain Environment

A Credit Management Mechanism for Peer-to-Peer Electricity T...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Jinhuan Guo Kaile Zhou Jie Chong Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

With the rapid development of distributed generation, peer-to-peer (P2P) electricity trading between microgrids in blockchain environment has become one of the indispensable ways to consume the local renewable energy. The existing credit management for P2P electricity trading generally realized through the smart contract of penalty, which cannot show the risk identification and risk aversion behaviors of each microgrid. Therefore, this study proposes a newly credit management mechanism to reduce the defaults, which achieves credit-based risk control optimization and credit-based continuous double auctions. In the first stage, microgrids make robust control optimize to submit a trading strategy that follows contracts as far as possible. In the second stage, which realizes rewards and penalties through credit-based equivalent price conversion. The experimental results show that the proposed credit management mechanism can improve microgrids' interests and ensure fair environment.

关键词： Robust control Renewable energy sources Smart contracts Microgrids System integration Power markets Real-time systems

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Distributionally Robust optimization for Internet Data Center with Renewable Energy

Distributionally Robust Optimization for Internet Data Cente...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zhineng Fei Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

More and more internet data centers (IDCs) are trying to use renewable energy sources (RESs). However, powering IDCs with intermittent RESs presents a significant challenge. In addition, power and workload management in IDCs has great potential to reduce energy consumption, carbon footprints and energy cost. This study proposes an optimal load dispatch model for an IDC with battery energy storage system (BESS), which aims to lower the total costs. To accommodate the uncertainties of wind power, a data-driven distributionally robust optimization (DDRO) method is adopted. Then the column-and-constraint generation method (C&CG) is used to solve the corresponding optimization problem. In the experiment with the real-world traces of workload arrival and wholesale electricity price, three scheduling scenarios are investigated. Moreover, robust optimization and stochastic programming are implemented for comparison. Experimental results reveal that total costs of the IDC can be effectively reduced by adopting BESS and implementing workload dispatch. Meanwhile, the results also demonstrate the effectiveness of DDRO approach.

关键词： Renewable energy sources Data centers Costs Uncertainty Stochastic processes System integration Wind power generation

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Research on Whole-Link Risk Situational Awareness Index System and Dynamic Risk Pool Supervision 11

Research on Whole-Link Risk Situational Awareness Index Syst...

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11th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Sun, Jingchun Deng, Fei Du, Boya School of Management Xi'An Jiaotong University China Key Laboratory of the Ministry of Education for Process Control and Efficiency Engineering China College of Information Engineering Kunming University China Key Laboratory of Data Governance and Intelligent Decision Universities of Yunnan China

ISBN: (纸本)9781450398039

With the development of society, network security becomes more and more important. It is inevitable to establish the risk index system to effectively monitor the security of network. In the previous study, due to absence of risk indicators, the risk supervising system is not perfect. However, there are few researches on the whole-link risk index system, and the determination of risk indicators lacks basis. Therefore, this paper firstly constructs a new risk index system of situational awareness, which can avoid the omission of risk indicators. Then the sliding window is used to observe this system and the optimal window width is found to determine the dynamic risk pool. Finally, the indicators of dynamic risk pool are used to dynamically monitor the whole link risk index system. The whole technical framework not only proves advantages of the mature risk index system of situational awareness, but also overcomes the disadvantage that the risk index of the whole link cannot be determined, and the dynamic risk pool has the sharing characteristics in the whole process. © 2022 ACM.

关键词： Network security

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Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model

arXiv

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arXiv 2024年

作者： Zhou, Yonghang Zhu, Hongyi Chai, Yidong Jiang, Yuanchun Liu, Yezheng School of Management Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Department of Information Systems and Cyber Security University of Texas at San Antonio 1 UTSA Circle San AntonioTX78249 United States

Web attacks are one of the major and most persistent forms of cyber threats, which bring huge costs and losses to web application-based businesses. Various detection methods, such as signature-based, machine learning-based, and deep learning-based, have been proposed to identify web attacks. However, these methods either (1) heavily rely on accurate and complete rule design and feature engineering, which may not adapt to fast-evolving attacks, or (2) fail to estimate model uncertainty, which is essential to the trustworthiness of the prediction made by the model. In this study, we proposed an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model to detect web attacks from HTTP request payload data with the model uncertainty captured from the perspective of both data distribution and model parameters. The proposed UEDKL utilizes a deep kernel learning model to distinguish normal HTTP requests from different types of web attacks with model uncertainty estimated from data distribution perspective. Multiple deep kernel learning models were trained as base learners to capture the model uncertainty from model parameters perspective. An attention-based ensemble learning approach was designed to effectively integrate base learners' predictions and model uncertainty. We also proposed a new metric named High Uncertainty Ratio-F Score Curve to evaluate model uncertainty estimation. Experiments on BDCI and SRBH datasets demonstrated that the proposed UEDKL framework yields significant improvement in both web attack detection performance and uncertainty estimation quality compared to benchmark models. © 2024, CC BY.

关键词： Adversarial machine learning

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Media Attention and ESG Greenwashing: Evidence from Chinese Listed Companies

SSRN

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SSRN 2023年

作者： Jiang, Cuixia Li, Xiuxiu Xu, Qifa Liu, Junhang School of Management Hefei University of Technology Anhui Hefei230009 China Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance Ministry of Education Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China

We investigate the relationship between media attention and environmental, social, and governance (ESG) greenwashing using the data of Chinese A-share listed companies from 2011 to 2020. Our results show that media attention can inhibit corporate ESG greenwashing behavior. Moreover, this inhibiting effect is mainly caused by paper media and becomes more prominent in non-state-owned enterprises (non-SOEs). The mediation analysis proves that media attention plays an important role in improving investor attention, thereby inhibiting corporate ESG greenwashing. The moderation analysis indicates that the effect of media attention on inhibiting ESG greenwashing is weakened in companies with more analyst following and higher quality of internal control. Our study provides a new view on the governance of ESG greenwashing in China. © 2023, The Authors. All rights reserved.

关键词： Quality control

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Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

arXiv

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arXiv 2024年

作者： Jing, Erkang Liu, Yezheng Chai, Yidong Yu, Shuo Liu, Longshun Jiang, Yuanchun Wang, Yang School of Management Hefei University of Technology Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Anhui Hefei China School of Management Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management Anhui Hefei China Rawls College of Business Texas Tech University LubbockTX United States School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users’ preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users’ actual emotional states expressed through identical emotional words are homogeneous. They also assume that users’ music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user’s decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://***/jingrk/HDBN. Copyright © 2024, The Authors. All rights reserved.

关键词： Economic and social effects

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Experimental study on in-situ capture of gaseous heavy metals by calcium-doped kaolinite in furnace

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Journal of Southeast University(English Edition) 2022年第1期38卷 62-69页

作者： Zha Jianrui Huang Yaji Xia Zhipeng Zhu Zhicheng Key Laboratory of Process Optimization and Intelligent Decision of Ministry of Education Hefei University of TechnologyHefei 230009China School of Management Hefei University of TechnologyHefei 230009China School of Energy and Environment Southeast UniversityNanjing 210096China

For revealing the effect of calcium on heavy metal in-furnace capture by kaolinite,gaseous PbCl_(2) and CdCl_(2) adsorptions by calcium-doped kaolinite at high temperatures and their mechanisms were investigated through experiments in a fixed-bed furnace,where factors of temperatures and the mixing proportion of limestone and kaolinite were *** results show that the adsorption efficiency of cadmium by the mixed sorbents is lower than that by single sorbents,mainly due to the consumption of lime or limestone,whereas that of lead varies at different reaction *** kinds of areas,named as Ca-rich zone and Ca-low zone,are found in the sorbents of calcium-doped *** Ca-rich zone tends to lessen the capture efficiencies for the two metals,whereas the Ca-low zone can enhance their *** heterogeneous distributions of calcium have multiple effects on metal sorption,including the diffusion enhancement through the eutectic effect by forming low-melting-point substances,the inhibition by the competitive occupation of reaction sites,and the block of pores caused by sintering at a high temperature.

关键词： adsorption heavy metal high temperature kaolinite mineral reaction

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Optimal Operation Of Battery Energy Storage System In Industrial Park

Optimal Operation Of Battery Energy Storage System In Indust...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Zenghui Zhang Xinhui Lu Key Laboratory of Process Optimization and Intelligent Decision-making Schoolof Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

An industrial park containing distributed generations (DGs) can be seen as a microgrid. Due to the uncertainty and intermittency of the output of DGs, it is necessary to add battery energy storage system (BESS) in industrial parks. The battery state of health (SOH) is an important indicator of battery life. It is necessary to fully consider the battery SOH during the energy optimization of industrial parks. In this work, a two-stage model suitable for charge and discharge optimization of BESSs in industrial park microgrids is proposed. The first stage of the model is a charge and discharge scheduling of a BESS considering the SOH. This stage mainly schedules the charge and discharge time and the total amount of time. The second stage of the model is employed to further determine the charge and discharge allocation of all battery packs to achieve BESS life prolongation. The experimental results show that the introduction of SOH makes the scheduling results more realistic. Also, it balances the changes in the SOHs of all battery packs.

关键词： Schedules Job shop scheduling Uncertainty Rain Microgrids System integration Discharges (electric)

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Exploring the impact of COVID-19 on travel behavior based on EV charging big data

Exploring the impact of COVID-19 on travel behavior based on...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Kaile Zhou Dingding Hu Fangyi Li Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665434263

The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3 % compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events.

关键词： COVID-19 Reactive power Pandemics Atmospheric modeling Big Data Air quality Electric vehicle charging

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