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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Influence of coupling mechanism of loose layer and fault on multi-physical fields in mining areas

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国际煤炭科学技术学报（英文版） 2023年第6期10卷 120-136页

作者： Jin Luo Yingming Li Xiangrui Meng Qingbiao Guo Guangming Zhao State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001AnhuiChina School of Mining Engineering Anhui University of Sciences and TechnologyHuainan 232001AnhuiChina State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001AnhuiChina School of Spatial Informatics and Geomatics Engineering Anhui University of Sciences and TechnologyHuainan 232001AnhuiChina

Coal mining under the geological conditions of a loose layer will lead to the intensification of surface movement and deforma-tion,and mining under the geological conditions of a fault will lead to the living slip of a *** under both conditions will have a great impact on the safety of coal *** reveal the evolution law of the coupling mechanism of loose layer and fault on the multi-physical fields of overburden,the numerical simulation method is used to simulate the coupling of loose layer and fault with different thicknesses,analyze the changes of vertical stress on the key strata,the changes of surface subsidence,the evolution of elastic energy on the fault zone and the changes of activated slip area of the fault *** simulation analysis shows that the vertical stress change trend of the key strata gradually changes from the"V"shape to the"W"shape at the beginning of mining,and the vertical stress concentration will occur at the *** loose layer will promote surface subsidence,and the fault will hinder the surface subsidence to a certain *** loose layer and the fault alternately affect the surface *** elastic energy accumulation on the key strata is mainly concentrated on both sides of the *** elastic energy in the center of the goaf is *** elastic energy accumulation in the fault zone starts from the shallowly buried fault and gradually develops to the deeply buried *** instability of fault activation has gone through the initial stage of activation—the intensification stage of activation—the stable stage of *** the working conditions of no loose layer,thin loose layer,and thick loose layer,the fault zone is the first to undergo living slip,and under the action of an extra-thick loose layer,there is a certain lag in the activation slip of the fault zone.

关键词： Loose layer Fault Vertical stress Surface subsidence Elastic energy Activated slip

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An End-to-End Deep Learning QoS Prediction Model Based on Temporal Context and Feature Fusion

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IEEE Transactions on Services Computing 2025年第03期18卷 1232-1244页

作者： Zhang, Peiyun Fan, Jiajun Chen, Yutong Huang, Wenjun Zhu, Haibin Zhao, Qinglin Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China

Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the information that is conducive to prediction. Aiming at the above problem, an end-to-end deep learning QoS prediction model based on a temporal context and feature fusion is proposed. In the proposed model, three blocks are designed for QoS prediction. Firstly, a user-service encoding conversion block is designed to convert the one-hot encodings of users and services into the latent features of users and services, which can make full use of the data in sparse matrices. Then a time feature extraction block is designed to extract time features based on the time-varying characteristics of QoS values. Finally, the time features are fused with the latent features of users and services to predict QoS values. The experimental results show that on existing datasets, the proposed model has better prediction accuracy than other advanced methods in response time and throughput. © 2025 IEEE.

关键词： Prediction models

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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GazeREC-Net: Advancing Gaze Restoration in Low-Light Conditions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2034-2042页

作者： Ku, Jiayin Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Tech nology Liaoning Anshan114051 China

Gaze estimation technology is essential for applications such as human-computer interaction, augmented reality, and virtual reality. However, its accuracy is significantly compromised in low-light conditions due to degraded image quality. To address this, we developed GazeREC-Net, an innovative gaze restoration method. We simulated low-light conditions on the MPIIFaceGaze and ColumbiaGaze datasets, creating a specialized degraded dataset for training and testing our model. GazeREC-Net combines Fourier transform techniques with advanced image restoration algorithms, significantly enhancing image quality and optimizing gaze information recovery and extraction in low-light environments. Our evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics demonstrated a PSNR improvement of 7.64% and an SSIM improvement of 5.75% compared to HINet. Additionally, GazeREC-Net outperformed CFTNet and other existing gaze estimation models, including Gaze-TR, Dilated-Net, CA-Net, L2CS-Net, and MTGLS, in reducing gaze estimation error. These findings validate the effectiveness of GazeREC-Net in low-light conditions and offer new research directions for applying gaze estimation technologies in complex lighting environments. © (2024), (International Association of Engineers). All rights reserved.

关键词： Augmented reality

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Robust Regularization Design of Graph Neural Networks Against Adversarial Attacks Based on Lyapunov Theory

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Chinese Journal of Electronics 2024年第3期33卷 732-741页

作者： Wenjie YAN Ziqi LI Yongjun QI School of Artificial Intelligence Hebei University of Technology School of Computer Science and Engineering of North China Institute of Aerospace Engineering

The robustness of graph neural networks（GNNs） is a critical research topic in deep *** researchers have designed regularization methods to enhance the robustness of neural networks,but there is a lack of theoretical analysis on the principle of *** order to tackle the weakness of current robustness designing methods,this paper gives new insights into how to guarantee the robustness of GNNs.A novel regularization strategy named Lya-Reg is designed to guarantee the robustness of GNNs by Lyapunov *** results give new insights into how regularization can mitigate the various adversarial effects on different graph *** experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-theart methods such as L1-norm,L2-norm,L2-norm,Pro-GNN,PA-GNN and GARNET against various types of graph adversarial attacks.

关键词： Deep learning Graph neural network Robustness Lyapunov Regularization

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Tuning mechanical and electrical performances of B_(4)C-TiB_(2)ceramics in a two-step spark plasma sintering process

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Journal of Advanced Ceramics 2024年第4期13卷 518-528页

作者： Jun Zhao Xingshuo Zhang Zongning Ma Dong Wang Xing Jin Songlin Ran Advanced Ceramics Research Center School of Materials Science and EngineeringAnhui University of TechnologyMaanshan 243002China Anhui Engineering Research Center for High Efficiency Intelligent Photovoltaic Module Chaohu UniversityHefei 238024China

B_(4)C–TiB_(2)is an advanced electrically conductive ceramic with excellent mechanical and electrical discharge machinable *** is challenging and rewarding to achieve highly conductive and hard B_(4)C–TiB_(2)composites at a minimum content of conductive TiB2 that has inferior hardness but double specific gravity of the B_(4)C matrix.A novel strategy was used to construct conductive networks in B_(4)C‒15 vol%TiB2 composite ceramics with B_(4)C,TiC,and amorphous B as raw materials by a two-step spark plasma sintering *** influences of particle size matching between B_(4)C and TiC on the conducting of the strategy and the microstructure were discussed based on the selective matrix grain growth *** mechanical and electrical properties were also systematically *** B_(4)C–15 vol%TiB2 composite ceramic prepared from 10.29µm B_(4)C and 0.05µm TiC powders exhibited a perfect three-dimensional interconnected conductive network with a maximum electrical conductivity of 4.25×10^(4)S/m,together with excellent mechanical properties including flexural strength,Vickers hardness,and fracture toughness of 691±58 MPa,30.30±0.61 GPa,and 5.75±0.32 MPa·m^(1/2),respectively,while the composite obtained from 3.12µm B_(4)C and 0.8µm TiC powders had the best mechanical properties including flexural strength,Vickers hardness,and fracture toughness of 827±35 MPa,32.01±0.51 GPa,and 6.45±0.22 MPa·m^(1/2),together with a decent electrical conductivity of 0.65×10^(4)S/m.

关键词： B_(4)C-TiB_(2) mechanical properties electrical conductivity conductive network

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Introduction to the Special Issue on Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

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computer Modeling in engineering & sciences 2024年第10期141卷 7-11页

作者： Xiong Luo Yongqiang Cheng Zhifang Liao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Faculty of Technology University of SunderlandSunderlandSR60DDUK School of Computer Science and Engineering Central South UniversityChangsha410083China

With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human ***,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].

关键词： Intelligence bringing intelligent

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization

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Tsinghua science and technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Diluie: constructing diverse demonstrations of in-context learning with large language model for unified information extraction

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Neural Computing and Applications 2024年第22期36卷 13491-13512页

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Large language models (LLMs) have demonstrated promising in-context learning capabilities, especially with instructive prompts. However, recent studies have shown that existing large models still face challenges in specific information extraction (IE) tasks. Moreover, it could have more effectively utilized various prompts such as instruction tuning, diverse demonstrations of in-context learning, and long-range token sequences for assisting language modeling in understanding context. In this study, we propose DILUIE, a unified information extraction framework based on in-context learning with diverse demonstration examples. DILUIE is encoded with an EVA attention mechanism and incremental encoding technology. Based on the constructed diverse demonstrations, we expand the size of instances efficiently in both instruction tuning and in-context learning to gain insights into the potential benefits of utilizing diverse information extraction datasets. To deepen the understanding of context, we further design three auxiliary tasks to assist in aligning contextual semantics. Experimental results demonstrate that DILUIE achieves 2.23 and 2.53% improvements in terms of Micor-/Macor-F1 on average relative to the current state-of-the-art baseline, which also significantly outperforms the GPT-3.5-turbo in zero-shot settings, and the average token length of achieving the best performance over tasks is around 15k. Furthermore, we observe that in-context learning shows enhanced performance when provided with more demonstrations during multiple-shot instruction tuning (8 k). Additionally, increasing the length of instructions (10 k) can result in a more substantial improvement in the upper limits of scaling for in-context learning. Code is available on https://***/Phevos75/DILUIE. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Demonstrations

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