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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Fu, Jinrong Liu, Yiwen Mi, Chunqiao Peng, Xiaoning Xiao, Jianhua School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924486

In view of the serious issues commonly existing with coursework in Chinese universities at present, such as its original function weakening or being suppressed, its form being too abstract and lack of elaborate design. In order to resolve these problems observed in usual homework, this paper proposes a reform scheme for cloud coursework based on Extended Reality and Bloom model. It mainly includes five key parts, such as interaction, scene, comprehensive evaluation, digital twin and data analysis. In addition, the reform’s effectiveness is illustrated through amassing and comparing the feedback of college students for teaching. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training

arXiv

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arXiv 2025年

作者： Ou, Yixin Yao, Yunzhi Zhang, Ningyu Jin, Hui Sun, Jiacheng Deng, Shumin Li, Zhenguo Chen, Huajun Zhejiang University China Huawei Noah’s Ark Lab Canada National University of Singapore NUS-NCS Joint Lab Singapore Zhejiang Key Laboratory of Big Data Intelligent Computing China

Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations. We address this issue through the lens of knowledge circuit evolution, identifying computational subgraphs that facilitate knowledge storage and processing. Our systematic analysis of circuit evolution throughout continual pre-training reveals several key findings: (1) the acquisition of new knowledge is influenced by its relevance to preexisting knowledge;(2) the evolution of knowledge circuits exhibits a distinct phase shift from formation to optimization;(3) the evolution of knowledge circuits follows a deep-to-shallow pattern. These insights not only advance our theoretical understanding of the mechanisms of new knowledge acquisition in LLMs, but also provide potential implications for improving continual pre-training strategies to enhance model performance Copyright © 2025, The Authors. All rights reserved.

关键词： Knowledge acquisition

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sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection System for Securing IoT Networks 25

sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection Sys...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering

作者： Ovi Abul Hussain Zigang Chen Haihua Zhu Chongqing Key Laboratory of Cyberspace Security Monitoring and Governance & Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400712647

The widespread deployment of IoT devices has seen the emergence of a huge security threat, and IoT networks are the favorite targets for numerous cyberattacks. Due to the scarce resources in most IoT devices, the conventional intrusion detection systems (IDS) are unable to catch up with the enormous traffic in IoT networks. This paper suggests a new intrusion detection model based on CNN-LSTM, incorporating deep learning and statistical filtering to increase the intrusion detection in IoT networks. The model uses Convolutional Neural Networks (CNN) to capture the spatial relationships and Long Short-Term Memory (LSTM) networks to capture the temporal relationships in IoT network traffic. The model also uses statistical filtering techniques, including Median Filtering and Standard Deviation-based Filtering, to pre-process the traffic and eliminate the noise and the outliers, making the model's precision improved. The model is tested based on the Edge-IIoTset dataset, comprised of different diverse attack types, including Denial of Service (DoS), SQL injection, Ransomware, and Man-in-the-Middle (MITM). The model gives 94.99% precision, reflecting the model's precision in detecting different intrusions. This paper sets the efficacy in the use of the integration between the use of CNN-LSTM and statistical filtering to build an efficient, scalable, and precise intrusion detection model in IoT networks. The findings offer a real-time solution to increase IoT security, in particular, intrusion detection in IoT networks in real-time.

关键词： Anomaly Detection

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A keypoint-Based Object Detection Algorithm

A Keypoint-Based Object Detection Algorithm

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Xiangdan Hou Quansong Chen Hongpu Liu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Hebei Province Key Laboratory of Big Data Computing Tianjin China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

An improved algorithm is proposed for the omission and re-detection problems in the point cloud object detection method CenterPoint. The algorithm firstly adds Focal sparse convolution module to the feature extraction network to increase the proportion of important information in the features; then redefines the generation function of the detection head heatmap, and the supervisory signal generated by the elliptical Gaussian kernel is more closely matched to the real shape of the object to inhibit the generation of negative samples; finally, the overall regression of the bounding box information is applied to the training, so that the back propagation loss gain of the single dimensional information is regulated by the associated information. The improved algorithm is trained and evaluated on the KITTI 3D object detection dataset, and the overall mean Average Precision (mAP) is improved by 3.29% compared with the base model CenterPoint. The experimental results show that the improved algorithm effectively suppresses the redetection and omission problems of CenterPoint.

关键词： Location awareness Training Solid modeling Three-dimensional displays Convolution Shape Object detection Feature extraction Propagation losses Kernel

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual Information 48

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Jicun Li, Xingjian Wang, Tianyang Wang, Shi Cao, Yanan Xu, Chengzhong Dou, Dejing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Big Data Lab Baidu Research China University of Alabama Birmingham United States Institute of Information Engineering Chinese Academy of Sciences China State Key Lab of Iotsc University of Macau China

ISBN: (纸本)9781728163277

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method. © 2023 IEEE.

关键词： Fine-tuning Stability Mutual Information Pre-trained Language Model

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Lightweight spatial pyramid pooling network for real-time semantic segmentation

Lightweight spatial pyramid pooling network for real-time se...

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2021 International Symposium on intelligent Robotics and Systems, ISoIRS 2021

作者： Jiang, Peilong Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China

In recent years, the state-of-the-art semantic segmentation models have made extremely successful in various challenging scenes. However, the high computation costs of these models make it difficult to deploy to mobile devices. To better serve in computation constraint scenes, the semantic segmentation model should not only have high segmentation performance, but also fast inference speed. In this paper, we proposed an efficient multi-scale context module named LSPPM, which can gather abundant context information at a low computation cost. Base on this, we present a real-time semantic segmentation model called LSPPNet which is specially designed for real-time application. We have done an exhaustive experiment to evaluate LSPPNet in the challenge urban street scenes datasets Cityscapes. Extensive experiment shows that LSPPNet gets a better trade-off between segmentation performance and inference speed. We test LSPPNet on an NVIDIA 2080 super graphics card and it can achieve 75.8% MIoU in Cityscapes test set in real-time speed. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Teaching Reform and Practice for the Course of Web Application Development Technology Under the Background of Online Education 17th

Teaching Reform and Practice for the Course of Web Applicati...

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17th International Conference on Computer Science and Education, ICCSE 2022

作者： Dong, Yi Xiaoning, Peng Xian, Zhang Chunqiao, Mi Yiwen, Liu Taiguo, Qu School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819924455

With the advent of knowledge economy, online education relies on the Internet and mobile terminals, it breaks the limitation of time and space of traditional education. Online education has gradually becoming a new way for people to learn. Under the special background of COVID-19 since 2020, online education has been well applied. The course of Web Application Development Technology is not only a course which is the core course in software development for computer majors, but also is a course with strong practicality and application. This paper analyzes the problems existing in the online teaching of the courses of web course under the background of online education, and presents a new scheme of the teaching reform measures for the course according to the characteristics of online education and the course of Web Application Development Technology which is well applied by the author’s own practices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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A Class-Specific Attribute Reduction Acceleration Algorithm Based on Neighborhood Rough Set 11th

A Class-Specific Attribute Reduction Acceleration Algorithm ...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xia, Mingfei Hu, Jun Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

ISBN: (纸本)9783031927461

Neighborhood rough sets are proficient in managing continuous data, but the selection of the neighborhood radius often necessitates multiple tests, leading to substantial time expenditure. To solve this problem, this paper proposes an acceleration algorithm for class-specific attribute reduction. Initially, the concept of class-specific multi-granularity reduction within the framework of neighborhood rough sets is clarified. The relationship between reductions induced by different neighborhood radii is demonstrated by analyzing the variation of approximate quality across different granularities. Subsequently, by integrating local neighborhood rough sets with sequential three-way decision, the sets of candidate samples and attributes are simultaneously reduced at a single granularity, thereby further diminishing the computational complexity. Finally, the corresponding multi-granularity acceleration algorithm is proposed. The experimental results derived from ten UCI datasets indicate that the proposed algorithm significantly decreases the time required for class-specific attribute reduction while maintaining the classification capability of neighborhood decision systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Acceleration Algorithm Class-Specific Attribute Reduction Multi-Granularity Neighborhood Radii Neighborhood Rough Sets

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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

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Research in Astronomy and Astrophysics 2023年第5期23卷 52-65页

作者： Hai-Feng Yang Xiao-Na Yin Jiang-Hui Cai Yu-Qing Yang A-Li Luo Zhong-Rui Bai Li-Chan Zhou Xu-Jun Zhao Ya-Ling Xun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology North University of ChinaTaiyuan 030051China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.

关键词： methods data analysis-surveys-techniques spectroscopic-site testing-methods analytical

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