关键词：
Carbon Generalized System of Preferences;Carbon emissions reduction;Evolutionary game theory;Public goods game with environmental;feedback;Second-order social dilemma;Phase transition
作者机构：
[Cai, Qihang; Niu, Lei] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Fac Artificial Intelligence Educ, Wuhan 430000, Peoples R China.
通讯机构：
[Niu, L ] C;Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Fac Artificial Intelligence Educ, Wuhan 430000, Peoples R China.
作者：
Yao, Shixiong;Tian, Xingjian;Chen, Jiageng*;Xiong, Yi
期刊：
International Journal of Network Management,2023年33(3):e2193- ISSN：1055-7148
通讯作者：
Chen, Jiageng
作者机构：
[Xiong, Yi; Yao, Shixiong; Chen, Jiageng] Cent China Normal Univ, Comp Sch, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.;[Yao, Shixiong] Wuhan Univ, Key Lab Aerosp Informat Secur & Trust Comp, Minist Educ, Wuhan, Peoples R China.;[Tian, Xingjian] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Peoples R China.
通讯机构：
[Chen, Jiageng] C;Cent China Normal Univ, Comp Sch, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
关键词：
Electric power transmission networks;Investments;Privacy-preserving techniques;Renewable energy resources;Smart power grids;Block-chain;Power;Privacy preservation;Privacy preserving;Renewable energy source;Smart grid;Smart grid systems;Transmission channels;User information;WEB application;Data visualization
摘要：
Smart grid has drawn a lot of attention and investment in recent years, which not only helps the modern generation and distribution of traditional power but also highly widens the application of renewable energy sources. However, the main challenges in the application of smart grid are 1. the privacy preservation of users' information and 2. the trustful transmission channel among peers. In order to solve these problems, VPN and blockchain can be considered since they have some features perfectly suitable for these situations. In this paper, we propose a smart grid system based on WireGuard and Hyperledger Fabric to solve the problems mentioned above. And we also implement the whole system and give a view by web application. What's more, all the functionalities are displayed and tested, including building a smart device simulator, deploying data visualization and making some performance evaluations about transactions and WireGuard communication. Experiment results show that the introduction of WireGuard into network infrastructure does not cause too much loss of bandwidth and delay, but it ensures a certain degree of communication security. And Fabric provides the consistency and traceability of transactions in smart grid system.
期刊：
IEEE Transactions on Instrumentation and Measurement,2023年72:1-12 ISSN：0018-9456
通讯作者：
Zhu, HY
作者机构：
[Zhao, Jinhua; Zhu, Hongye] Cent ChinaNormal Univ, Cent China Normal Univ Wollongong Joint Inst, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Zhu, Hongye] Cent ChinaNormal Univ, Cent China Normal Univ Wollongong Joint Inst, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
通讯机构：
[Zhu, HY ] ;Cent ChinaNormal Univ, Cent China Normal Univ Wollongong Joint Inst, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
作者机构：
[Li, Linqing; Wang, Zhifeng] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Peoples R China.;[Wang, Zhifeng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
通讯机构：
[Wang, ZF ] C;Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Peoples R China.;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Peoples R China.
摘要：
Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing. Our model mitigates the impact of subjective labeling by fine-tuning the skill relation matrix and Q-matrix. Additionally, we employ Graph Convolutional Networks (GCNs) to capture intricate interactions between students, exercises, and skills. Specifically, the Knowledge Relation Importance Rank Calibration method is employed to generate the skill relation matrix and Q-matrix. These calibrated matrices, alongside heterogeneous interactions, serve as input for the GCN to compute exercise and skill embeddings. Subsequently, exercise embeddings, skill embeddings, item difficulty, and contingency tables collectively contribute to an exercise relation matrix, which is then fed into an attention mechanism for predictions. Experimental evaluations on two publicly available educational datasets demonstrate the superiority of our proposed model over baseline models, evidenced by enhanced performance across three evaluation metrics.
期刊：
Applied Mathematics and Computation,2022年413:126623 ISSN：0096-3003
通讯作者：
Zhao, Jinhua
作者机构：
[Kerivin, Herve] CNRS, LIMOS, UMR 6158, F-63178 Aubiere, France.;[Zhao, Jinhua] Cent China Normal Univ, Fac Artificial Intelligence Educ, Cent China Normal Univ Wollongong Joint Inst, Wuhan 430079, Peoples R China.
通讯机构：
[Zhao, Jinhua] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Cent China Normal Univ Wollongong Joint Inst, Wuhan 430079, Peoples R China.
作者机构：
[Xingshen Liu; Lei Niu] Faculty of Artificial Intelligence in Education, Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China
会议名称：
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
会议时间：
05 December 2021
会议地点：
Wuhan, Hubei Province, China
会议论文集名称：
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
摘要：
Educational Data Mining (EDM) has been a popular research topic in education, and many current studies use EDM techniques to predict student performance, so that the teachers and students can understand the student's performance in real-time and further develop the learning plan for the students. However, current work is often not sufficiently accurate in predicting student performance. Firstly, the students' features are not adequately processed, resulting in a large amount of noise data in the student dataset, affecting the prediction results. Secondly, there is still space for improvement in the current studies on the student performance prediction model. Therefore, in this paper, the Multi-Agent System (MAS) idea is used to propose an Agent-based Modeling Feature Selection(ABMFS) model, and the selected feature subset effectively removes the features that are irrelevant to the prediction results. Next, the Deep Learning techniques are used to construct a Convolutional Neural Network (CNN) based structure to predict student performance. The result of the experiments shows that the ABMFS Model selects the targeted features and improved performance noticeably across different classifiers, and better prediction results are achieved when the proposed approach was used for student performance prediction.
作者机构：
[Litian Huang] Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China;[Lei Niu] Central China Normal University Wollongong Joint Institute, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
会议名称：
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
会议时间：
05 December 2021
会议地点：
Wuhan, Hubei Province, China
会议论文集名称：
2021 IEEE International Conference on Engineering, Technology & Education (TALE)
摘要：
Today, many educational institutions still use man-ual methods for Course Offering Determination (COD) problems, leading to many problems such as increased workload and time conflict. The COD problem is a vital research topic because the preferences of both students and the educational institute as well as various resource constraints should be satisfied. By adopting the advantages of the Multi-Agent System (MAS), this paper proposes a course-offering approach, which is responsible for selecting the courses to be offered in the upcoming semester for a group of students. In the course-offering approach, the paper designs three student preference models to transform students' personal expectations and learning plans into their preference for courses. To aggregate students' preferences, the course-offering approach introduces a voting mechanism based on Single Transferable Vote (STV). Besides, the research also uses a bilateral single-issue negotiation model to improve the overall satisfaction of students and the educational institution.
摘要：
This paper formulates the climate change dilemma as an adaption of public goods game. The Nash equilibrium of the climate change dilemma is analyzed in the cases of discrete contribution and continuous contribution. Analytic results show that environmental feedback promotes cooperation to a certain extent, but as the number of players increases, zero contribution becomes the only Nash equilibrium in most cases. A dynamic model based on Particle Swarm Optimization algorithm is then proposed for the climate change dilemma, where the information exchange is restricted by a network. Simulation results show that the proposed dynamic model can effectively promote cooperation, especially in the case of continuous contribution, and making decisions based on historical information can largely boost the average contribution. (C) 2021 Elsevier Inc. All rights reserved.
期刊：
Journal of Systems Science and Systems Engineering,2021年30(4):417-432 ISSN：1004-3756
通讯作者：
Niu, Lei(lniu@ccnu.edu.cn)
作者机构：
[Huang, Litian; Yu, Xinguo; Niu, Lei] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan 430000, Peoples R China.;[Zhao, Jinhua] Wuhan Univ, Sch Econ & Management, Wuhan 430000, Peoples R China.;[Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430000, Peoples R China.
通讯机构：
[Lei Niu] C;Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China
摘要：
The research of multiple negotiations considering issue interdependence across negotiations is considered as a complex research topic in agent negotiation. In the multiple negotiations scenario, an agent conducts multiple negotiations with opponents for different negotiation goals, and issues in a single negotiation might be interdependent with issues in other negotiations. Moreover, the utility functions involved in multiple negotiations might be nonlinear, e.g., the issues involved in multiple negotiations are discrete. Considering this research problem, the current work may not well handle multiple interdependent negotiations with complex utility functions, where issues involved in utility functions are discrete. Regarding utility functions involving discrete issues, an agent may not find an offer exactly satisfying its expected utility during the negotiation process. Furthermore, as sub-offers on issues in every single negotiation might be restricted by the interdependence relationships with issues in other negotiations, it is even harder for the agent to find an offer satisfying the expected utility and all involved issue interdependence at the same time, leading to a high failure rate of processing multiple negotiations as a final outcome. To resolve this challenge, this paper presents a negotiation model for multiple negotiations, where interdependence exists between discrete issues across multiple negotiations. By introducing the formal definition of “interdependence between discrete issues across negotiations”, the proposed negotiation model applies the multiple alternating offers protocol, the clustered negotiation procedure and the proposed negotiation strategy to handle multiple interdependent negotiations with discrete issues. In the proposed strategy, the “tolerance value” is introduced as an agent’s consideration to balance between the overall negotiation goal and the negotiation outcomes. The experimental results show that, 1) the proposed model well handles the multiple negotiations with interdependence between discrete issues, 2) the proposed approach is able to help agents in the decision-making process of proposing acceptable offers, 3) an agent can choose a proper “tolerance value” to balance between the success rate of multiple negotiations and its expected utility.
作者机构：
[Xiao, Yao] Cent China Normal Univ, Wollongong Joint Inst, Wuhan 430079, Peoples R China.;[Zhou, Guangyou] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Zhou, Guangyou] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
关键词：
Syntactics;Solid modeling;Semantics;Manganese;Encoding;Sentiment analysis;Natural language processing;sentiment analysis;text mining;graph convolutional networks
摘要：
Aspect-level sentiment classification is a hot research topic in natural language processing (NLP). One of the key challenges is that how to develop effective algorithms to model the relationships between aspects and opinion words appeared in a sentence. Among the various methods proposed in the literature, the graph convolutional networks (GCNs) achieve the promising results due to their good ability to capture the long distance between the aspects and the opinion words. However, the existing methods cannot effectively leverage the edge information of dependency parsing tree, resulting in the sub-optimal results. In this article, we propose a syntactic edge-enhanced graph convolutional network (ASEGCN) for aspect-level sentiment classification with interactive attention. Our proposed method can effectively learn better representations of aspects and the opinion words by considering the different types of neighborhoods with the edge constraint. To evaluate the effectiveness of our proposed method, we conduct the experiments on five standard sentiment classification results. Our results demonstrate that our proposed method obtains the better performance than the state-of-the-art models on four datasets, and achieves a comparative performance on Rest16.
