In traditional education, there is not much difference between assessment tasks designed for learners. However, learners' learning performance may vary due to a number of factors, e.g., learning ability, academic emotion, and learners' and teachers' academic expectations. Considering those factors, accurately recommending personalized assessment tasks for each learner is challenging. To overcome the limitations in the current work, this paper proposed an autonomous-agent-based approach to recommend personalized assessment tasks considering multiple factors. Contributions of the proposed approach contain three aspects: (1) Considering objective factors, the proposed approach involves dynamically adjusting the assessment tasks recommended for students by applying both item response theory and the proposed academic emotion influence model. (2) Considering subjective factors, the proposed approach can dynamically predict learners' learning performances by applying autonomous agent-based negotiation. (3) The proposed recommendation algorithm based on discrete linear programming can effectively address the issue of cold start in typical recommendation algorithms. The experiments conducted in this paper demonstrate that the proposed approach effectively recommends assessment tasks for learners by considering both objective and subjective factors. The results indicate that this approach generates better recommendation outcomes than traditional content-based and collaborative filtering recommendation algorithms. Furthermore, the experiments reveal that the teacher's personality is the primary factor affecting the recommendation results, while the degree of similarity between the teacher's and learner's personality also plays a role.
Recognizing classroom behavior is crucial for assessing and improving teaching quality. However, the existing methods for behavior recognition have limited accuracy due to issues, such as occlusions, pose variations, and inconsistent target scales. To address these challenges, we propose an advanced single-stage object detector called ConvNeXt Block Prediction Head Network (CBPH-Net). Specifically, we design an efficient feature extraction module (FEM) to capture more channel information and relevant features from the images in the backbone network. The neck network combines the path aggregation network (PANet) architecture and coordinate attention (CA) to integrate semantic and positional information and suppress irrelevant background information, enabling the network to accurately locate students. CBPH utilizes convolutional kernels of different sizes and parsing multiscale features to enhance the multiscale recognition capability of CBPH-Net especially for accurate detection of small objects. To reduce the influence of irrelevant background, we use elliptical boxes instead of rectangular boxes when calculating the similarity between ground-truth and predicted values. In addition, we construct a dataset named Student-Teacher Behavior Dataset (STBD-08) that contains 4432 images with 151574 labeled anchors covering eight typical classroom behaviors. On the proposed dataset STBD-08, CBPH-Net achieves a mean average precision (mAP) of 87.5% (an improvement of 3.4% compared with YOLOv5 and 1.2% compared with YOLOv7). It processes one frame with the latency of 31.3 ms (1 ms slower than YOLOv5 and 5.3 ms faster than YOLOv7). Moreover, it achieves a precision of 75.7% in small object recognition, surpassing all comparative methods. The experimental results demonstrate that the CBPH-Net can be efficiently applied to classroom behavior recognition tasks. Codes and datasets are available at https://github.com/icedle/CBPH-Net.
Yao, Shixiong;Tian, Xingjian;Chen, Jiageng*;Xiong, Yi
International Journal of Network Management,2021年33(3) ISSN：1055-7148
[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.
[Chen, Jiageng] C;Cent China Normal Univ, Comp Sch, 152 Luoyu Rd, Wuhan, Hubei, Peoples R China.
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.
Journal of Systems Science and Systems Engineering,2021年30(4):417-432 ISSN：1004-3756
[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.
[Lei Niu] C;Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China
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.
[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.
[Zhou, Guangyou] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
Syntactics;Solid modeling;Semantics;Manganese;Encoding;Sentiment analysis;Natural language processing;sentiment analysis;text mining;graph convolutional networks
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.