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7th International Conference on Educational technology Management, ICETM 2024

作者： Zhao, Dong Zhang, Dan Ma, Xiujuan School of Computer Qinghai Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400717468

Generative Artificial Intelligence (GenAI) is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, the use of GenAI in education raises ethical concerns such as the potential impact on critical thinking skills and the unethical or dishonest use by students. This paper analyzed the main problems existing in engineering courses in Chinese universities firstly and then proposed corresponding teaching reform measures based on the comprehensive consideration of the benefits and threats of GenAI. We applied the proposed teaching reform measures in the teaching of engineering courses, which greatly improved the teaching quality. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Scalable Algorithms for Forest-Based Centrality on Large Graphs 25

Scalable Algorithms for Forest-Based Centrality on Large Gra...

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34th ACM Web Conference, WWW 2025

作者： Sun, Yubo Sun, Haoxin Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Institute of Intelligent Complex Systems Fudan University Shanghai200433 China

ISBN: (纸本)9798400712746

Centrality measures are essential for identifying important nodes and edges in networks. In this paper, we focus on two forest-based centrality measures on undirected graphs: forest node centrality (FNC) and forest edge centrality (FEC), which capture the influence of nodes and edges through their participation in spanning forests. Both centrality measures can be represented using entries of the forest matrix. To address the challenge of computing the two measures on large networks, we propose two scalable algorithms from different perspectives. The first algorithm IFGN combines two variance reduction techniques to approximate the entries of the forest matrix, applicable to both FNC and FEC. The second algorithm FECE incorporates a new physical interpretation of FEC, allowing for a better overall estimation. We provide error guarantees for both algorithms and demonstrate their efficiency and effectiveness through extensive experiments on various real-world networks. © 2025 Copyright held by the owner/author(s).

关键词： Approximation algorithms

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An Amalgamated CNN-Transformer Network for Lightweight Image Super-Resolution

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1376-1387页

作者： Fang, Jinsheng Lin, Hanjiang Zeng, Kun School of Computer Science and Engineering Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Fujian Zhangzhou363000 China School of Computer and Control engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China

Recently, Transformer-based methods for single image super-resolution (SISR) have achieved better performance advantages than the methods based on convolutional neural network (CNN). Exploiting self-attention mechanism to model global context definitely improves the SR results. However, the neglect of local information will bring inevitable reduction of the network performance. In this work, we propose an Amalgamated CNN-Transformer network for lightweight SR, namely ACTSR. Specifically, an amalgamated CNN-Transformer block (ACTB) is developed to extract the useful information of both local and global features. By employing stacked ACTBs, our ACTSR extracts more informative deep features beneficially for super-resolution reconstruction to improve network performance while keeps lightweight and flexible enough. Extensive experiments on commonly used benchmark datasets validate our ACTSR outperforms the advanced competitors. Our codes are available at: https://***/ginsengf/ACTSR. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Robust Video Object Segmentation with Restricted Attention 48

Robust Video Object Segmentation with Restricted Attention

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

作者： Zhang, Huaizheng Guo, Pinxue Le, Zhongwen Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing China Fudan University School of Computer Science Shanghai China Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9781728163277

This paper focuses on the two problems of the similar objects distraction and the lack of robustness for unseen object categories in semi-supervised video object segmentation task. Existing methods have achieved great results on the benchmark dataset, but these two problems still have not been completely solved. We propose the Robust Video Object Segmentation With Restricted Attention (RVOSR), which can suppress the effects caused by similar objects and filter out noise confusion from other irrelevant regions. Meanwhile augmenting the semantic information of features, which makes the features more suitable for video object segmentation task. Extensive experiments demonstrate the effectiveness of our approach and achieve the state-of-the-art performance on the widely-used VOS benchmarks including DAVIS-2016 (92.1% J& F), DAVIS-2017 (86.8% J& F) and YouTubeVOS-2019 (84.8%). © 2023 IEEE.

关键词： Semantics

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Dual-branch Transformer with Series Decomposition for Long-term Orbit Correction

Dual-branch Transformer with Series Decomposition for Long-t...

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2024 International Technical Meeting of The Institute of Navigation, ITM 2024

作者： Liu, Ziyu Wang, Jingran Zheng, Shaolong Wei, Shiyi Li, Zhenni Xie, Shengli Wang, Mingwei School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education China

ISBN: (纸本)9780936406367

Time to First Fix (TTFF) is one of the most important measures of GNSS receiver performance. Under the hot-start mode, the receiver needs only 1 to 2 seconds for positioning. However, under the cold start mode, it takes more than 20 seconds to receive the ephemeris to obtain the accurate satellite position. This is due to the fact that current satellite orbit prediction method cannot provide position information accurately based on the old ephemeris, which is detrimental to fast positioning. To address the above challenge, we propose a novel Transformer-based model for long-term orbit corrections. The model makes full use of the time-series information of historical orbit errors and is able to capture long-term dependencies from orbit error series in different lengths. The predicted orbits can be corrected according to the predicted error components, thereby achieving accurate satellite orbit prediction. Due to the temporal patterns in the orbit error series, the features and information hidden in their complex temporal patterns are often difficult to be extracted and utilised. To tackle the intricate temporal patterns, we introduce an inner series decomposition block to deconstruct the seasonal part and trend-cyclical part from orbit errors series, which also empowers the model with immanent decomposition capacity. In addition, in order to extract the cross-time dependency within the curves as well as the cross-dimension dependency between the curves, we propose the Dual-Branch Attention (DBA) layer to capture the cross-time and cross-dimension dependency. Integrating the proposed series decomposition block and the Dual-Branch Attention(DBA) layer into Transformer, we build a Dual-branch Transformer with series decomposition (DBSD-Transformer) for long-term orbit corrections. Experimental results on satellite orbit error data in 2022 from the BeiDou MEO-10 satellite show the effectiveness of our method. Compared to the prediction method based on the Kepler model, t

关键词： Orbits

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Enhanced YOLOv7-Based Construction Site Environmental Detection Method 4

Enhanced YOLOv7-Based Construction Site Environmental Detect...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Liang, Siheng Huang, Wenming Xiao, Yannan Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China

ISBN: (纸本)9798350343618

This paper proposes an enhanced approach utilizing the YOLOv7 model for automated construction site environment detection and management. The method leverages high-resolution surveillance cameras and computer vision techniques to achieve this goal. The proposed method starts with data collection of the construction site environment. Then produce accurate bounding boxes. These bounding boxes serve as input to an improved YOLOv7 network model, enabling feature extraction and generating high-scoring feature maps with associated weights. Utilizing these weights, image segmentation is performed to obtain the desired detection heads. Experimental results demonstrate the effectiveness of the method in identifying and annotating various environmental features within the construction site, leading to significant improvements in detection accuracy. Compared to similar techniques, this method offers faster detection speed, higher accuracy, and reduced error rates. The proposed construction site environment detection method provides an efficient and automated solution for on-site management in the construction industry, with practical applications and value. © 2023 IEEE.

关键词： Construction industry

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Research on Double Attention Mechanism High-resolution Network for Human Pose Estimation

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Engineering Letters 2025年第2期33卷 338-347页

作者： Li, Haisheng Yuan, Rongrong Chen, Jingyin Li, Qiuyi Hu, Cong Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guangxi Guilin541004 China School of Electronic and Information Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guangxi Guilin541004 China

Recently, high-resolution networks combining different resolution features have achieved good results in human pose estimation tasks. However, there are still some problems for high-resolution networks, such as insufficient use of important feature information and lost information in the sampling process. This paper proposes a double attention mechanism high-resolution network based on human pose estimation for these problems. The double attention mechanism consists of coordinate and downsampling attention modules. The coordinate attention module can enhance the feature representation of the network by extracting more useful node information. The downsampling attention module reuses the initial feature information to predict the occluded parts better and accurately estimate the corresponding point positions of human joints. Experimental results on MPII and COCO datasets show that the proposed network model effectively improves the accuracy of keypoint estimation in human pose estimation tasks. Especially, our network model achieves 78.1% average precision (AP) on the COCO dataset. © 2025, International Association of Engineers. All rights reserved.

关键词： Importance sampling

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Preserving Topology of Network Systems: Metric, Analysis, and Optimal Design

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IEEE Transactions on Automatic Control 2024年第6期70卷 3540-3555页

作者： Li, Yushan Wang, Zitong He, Jianping Chen, Cailian Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Preserving the topology from being inferred by external adversaries has become a paramount security issue for network systems (NSs), and adding random noises to the nodal states provides a promising way. Nevertheless, recent works have revealed that the topology cannot be preserved under i.i.d. noises in the asymptotic sense. How to effectively characterize the non-asymptotic preservation performance still remains an open issue. Inspired by the deviation quantification of concentration inequalities, this paper proposes a novel metric named trace-based variance-expectation ratio. This metric effectively captures the decaying rate of the topology inference error, where a slower rate indicates better non-asymptotic preservation performance. We prove that the inference error will always decay to zero asymptotically, as long as the added noises are non-increasing and independent (milder than the i.i.d. condition). Then, the optimal noise design that produces the slowest decaying rate for the error is obtained. More importantly, we amend the noise design by introducing one-lag time dependence, achieving the zero state deviation and the non-zero topology inference error in the asymptotic sense simultaneously. Extensions to a general class of noises with multi-lag time dependence are provided. Comprehensive simulations verify the theoretical findings. © 1963-2012 IEEE.

关键词： Network topology

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MATL: A deep neural network using multi-scale convolutions and transformer for transcription factor binding site prediction

MATL: A deep neural network using multi-scale convolutions a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Jinli Shen, Ruyi Huang, Xiaodi Zhang, Yanju Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China College of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9798350386226

Transcription factors bind to specific sequences of DNA known as transcription factor binding sites (TFBSs) in order to regulate gene expression. Identifying TFBSs is crucial for deciphering gene expression mechanisms, understanding in vitro life characteristics, and drug design. In recent years, many deep learning methods that utilize only the sequence features or a combination of sequence and shape features have been developed to predict TFBSs. However, these methods have not fully considered the characteristics of TFBSs, and their encoding strategies are relatively simplistic, thereby failing to capture the complex dependencies hidden within TFBSs. To address these issues, we propose a novel deep neural network model called MATL, which integrates sequence and shape features for TFBSs prediction. The model employs techniques such as multi-scale convolution and Transformer, and combines different modules for feature extraction based on the distribution patterns of different feature matrices. Additionally, we introduce transfer learning to address the issue of unsatisfactory prediction results due to insufficient training data in some datasets. As the results indicate, MATL exhibits excellent predictive performance on 165 validated ChIP-seq datasets, surpassing state-of-the-art methods. We also conducted ablation experiments on sequence encoding, feature extraction, and shape modules used in this study, and demonstrated that these modules play a positive role in predicting TFBSs. Moreover, the results also indicated that transfer learning can improve prediction accuracy for some TFBSs datasets with small amounts of data. The source code of MATL is available at https://***/LittleWhiteKing/MATL. © 2024 IEEE.

关键词： Transcription

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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