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Natural Language Processing (ICNLP), International Conference on

作者： Sugang Ma Siwei Sun Lei Pu Xiaobao Yang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China School of Operational Support Rocket Force Engineering University Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese Network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network’s ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%; On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements.

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Seasonal Variation, Gas/Particle Partitioning, Sources and Risks of Polycyclic Aromatic Compounds in Xi’an, Northwest China

SSRN

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SSRN 2023年

作者： Zhang, Jianing Wei, Chong Han, Yongming Lei, Dewen State Key Laboratory of Loess and Quaternary Geology Key Lab of Aerosol Chemistry and Physics Institute of Earth Environment Chinese Academy of Sciences Xi’an710061 China Shanghai Carbon Data Research Center CAS Key Laboratory of Low-Carbon Conversion Science and Engineering Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Department of Environmental Science and Technology School of Human Settlements and Civil Engineering Xi’an Jiaotong University Xi’an710049 China University of Chinese Academy of Sciences Beijing100049 China

Polycyclic aromatic compounds (PACs), such as polycyclic aromatic hydrocarbons (PAHs,) and their derivatives (oxygenated PAHs, nitrated PAHs and N-heterocyclic PAHs), have received extensive attention due to their carcinogenicity, teratogenicity, and mutagenicity to humans. In this study, gaseous and PM2.5-bound PACs were analysed in four seasons in 2016 in Xi’an and further evaluated for their gas/particle partition, sources, and health risks. Annual average concentrations (gaseous + particulate) of Σ27PAHs, Σ12OPAHs, Σ14NPAHs, and Σ4AZAs were 445.1±428.0, 45.6±62.4, 4.9±6.5, and 6.2±8.6 ng m−3, respectively, with the highest values in winter and the lowest values in summer. Low-molecular-weight PACs mainly occur in gaseous and high-molecular-weight PACs partition in particles, and the major mechanism is absorption into the atmospheric particles. PACs significantly correlated with combustion-derived air pollutants and carbon fractions, suggesting their common sources and transport pathways. The concentration ratios indicated mixed sources of fossil fuel combustion and biomass burning, while the positive matrix factorization model suggested that coal combustion, vehicle exhaust, and biomass burning accounted for 30.0%, 25.8% and 23.6% of PAHs, respectively, and direct emissions contributed 56.1% of OPAHs. The PACs estimated annual lifetime lung cancer risk is 2.67 × 10−5, exceeding the value advocated by the World Health Organization (10−5). © 2023, The Authors. All rights reserved.

关键词： Polycyclic aromatic hydrocarbons

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Tobacco Curing State Recognition Based on a Multi-Sensor Feature Fusion Approach

Tobacco Curing State Recognition Based on a Multi-Sensor Fea...

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International Conference on Computer and Communications (ICCC)

作者： Jili Zhang Yang Cao Jianyu Wei Jing Yang Mingxiong He Congguang Huang Banghong Wu Yabo Yu Zhengbin Huang Chenglin Rao Shiyong Li China Tobacco Guangxi Industrial Co. Ltd. Guangxi China Mechanical Engineering College Guizhou University Guizhou China State Key Laboratory of Public Big Data Guizhou University Guizhou China Hechi Branch of China Tobacco Guangxi Zhuang Autonomous Region Corporation. Guangxi China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The accurate identification of tobacco curing status is a complex process involving comprehensive analysis of multiple types of feature data sources. The influence of each type of feature on the state recognition model varies with the change in the curing process, and the existing models cannot reasonably utilize these feature data, which makes it difficult to improve the model recognition accuracy. To address these challenges, this paper proposes a dynamic data fusion scheme based on clustering methods and uses the LightGBM algorithm and data fusion method to build a state recognition fusion model that integrates six state recognition sub-models, which can accurately identify the curing status. SRFM aims to integrate curing expertise and automatically adjust the data fusion strategy according to the changes in multi-source data to meet the needs of comprehensive data analysis in recognition. Through experimental comparison, SRFM outperforms the existing models, with an accuracy of 99.8% in identifying the curing status and a macro F1-Score of 99.9%.

关键词： Temperature distribution Accuracy Computational modeling Heuristic algorithms Soft sensors data preprocessing data integration Curing Feature extraction data models

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Short-term traffic flow prediction based on BAS-VMD-CEEMDAN and IWOA-RF

Short-term traffic flow prediction based on BAS-VMD-CEEMDAN ...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Jia Tian Deyong Wang Yanyun Fn Xueyi Zhao Jian Fang Wenxi Shi School of Information Science and Engineering Xinjiang University Urumqi China Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Xinjiang Lianhaichuangzhi Information Technology Co. Ltd Urumqi China Beijing Academy of Science and Technology Beijing China

Short-term traffic flow forecasting is crucial for intelligent traffic management. In view of the non-linear and random nature of short-term traffic flow, a prediction model based on BAS-VMD-CEEMDAN and IWOA-RF is proposed. Firstly, the parameters of the VMD, namely the modal number (K) and the value of the quadratic penalty term (α), are optimized using the BAS algorithm, and then the traffic flow is decomposed to obtain several VMF components and one RES component, which extracts the smooth information of the traffic flow. Subsequently, the RES component is decomposed using the decomposition completeness of CEEMDAN to obtain several IMF components, refining the complex information of the RES component. Then, the RF model is constructed according to the characteristics of each component, and then the parameters of RF models are optimized using IWOA. Finally, overlay the predictions of each component superimposed to obtain the final predictions. Experimental results show that the short-term traffic flow forecasting model basedon BAS-VMD-CEEMDAN-IWOA-RF has a high accuracy compared to other decomposition models.

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Bio-Inspired Visual Neural Network for Moving Pedestrian Detection

SSRN

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SSRN 2023年

作者： Zhao, Qingqing Dai, Kunyu Hu, Bin Yang, Kai State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Information Engineering Guizhou Communications Polytechnic Guiyang550025 China

Reliably detecting pedestrian objects is crucial for building advanced intelligent surveillance systems in the future. In natural environments, traditional pedestrian detection methods are challenged by the irregular features of pedestrian objects in the field of view, such as varying scales, postures, and positions. Inspired by the mammalian visual layered structures and invariant object recognition mechanisms in biological vision systems, a novel feedforward visual neural network is investigated to detect moving pedestrian objects. The proposed model consists of two neural parts: the presynaptic and postsynaptic networks. The former captures visual signals, representing low-level features of a moving object, based on the significant convex and concave regions of the object’s contour;the latter processes these captured signals to form an invariant representation on behalf of a moving object, utilizing the invariant object recognition with sparse representation of biological recognition mechanism. Experimentally, the effectiveness and reliability of the proposed model are sufficiently verified by pedestrian videos in different realistic scenes, and comparative experimental results demonstrate that it outperforms existing state-of-the-art models. This research is a critical step towards the construction of an artificial vision system for pedestrian object perception. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Improved image captioning with subword units training and transformer

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High Technology Letters 2020年第2期26卷 211-216页

作者： Cai Qiang Li Jing Li Haisheng Zuo Min School of Computer and Information Engineering Beijing Techology and Business UniversityBeijing 100048P.R.China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing 100048P.R.China National Engineering Laboratory for Agri-Product Quality Traceability Beijing 100048P.R.China

Image captioning models typically operate with a fixed vocabulary,but captioning is an open-vocabulary *** work addresses the image captioning of out-of-vocabulary words by labeling it as unknown in a *** addition,recurrent neural network(RNN)and its variants used in the caption task have become a bottleneck for their generation quality and training time *** address these 2 essential problems,a simpler but more effective approach is proposed for generating open-vocabulary caption,long short-term memory(LSTM)unit is replaced with transformer as decoder for better caption quality and less training *** effectiveness of different word segmentation vocabulary and generation improvement of transformer over LSTM is discussed and it is proved that the improved models achieve state-of-the-art performance for the MSCOCO2014 image captioning tasks over a back-off dictionary baseline model.

关键词： image captioning transformer byte pair encoding(BPE) reinforcement learning

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Public emotions on Internet: In case of AIGC

arXiv

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arXiv 2023年

作者： Wei, Qinglan Li, Jiayi Zhang, Yuan School of Data Science and Intelligent Media Communication University of China Beijing100024 China School of Information and Communication Engineering Communication University of China Beijing100024 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

The proliferation of interactive AI like ChatGPT has fueled intense public discourse surrounding AI-generated content (AIGC). While some fear job displacement, others anticipate productivity gains. Social media provides a rich source of data reflecting public opinion, attitudes, and behaviors. By examining the factors influencing collective sentiment toward AIGC on various platforms, we can refine products, marketing, and AI models themselves. Our research utilized a novel system for real-time tracking and detailed visualization of public mood related to AIGC. This system enabled analysis of the dynamics shaping opinions on nine AIGC products across China's three leading social media sites. Our findings reveal a negative correlation between user demographics (age and education) and positive sentiment towards AIGC on Douyin, contrasting with Weibo's susceptibility to the rapid spread of extreme viewpoints. This work uniquely connects group dynamics theory with social media sentiment, offering valuable guidance for managing online opinion and tailoring targeted campaigns. Copyright © 2023, The Authors. All rights reserved.

关键词： Social psychology

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction Information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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Addr-Net: Attention Mechanism-Based Dual-Stream Deformable Medical Image Registration Network

SSRN

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SSRN 2024年

作者： Fan, Chao Zhu, Xinru Peng, Bincheng Xuan, Zhihui Zhu, Zhentong School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Medical image registration is crucial for tumor growth monitoring, radiation therapy, and disease diagnosis. Recently, U-Net type networks have become widely used in unsupervised image registration to predict dense deformation fields. However, for image data with large deformations, these networks are prone to interference from outliers and task-irrelevant regions, leading to suboptimal registration accuracy and difficulty in maintaining the continuity of the deformation field. To address this issue, we propose a novel model called ADDR-Net. ADDR-Net leverages dual encoders and incorporates a Cross-Perceptual Attention Module (CPA) alongside a Multi-dimensional Attention Module (MDA) to enhance registration. Unlike traditional methods that concatenate fixed and moving images into a single encoder, ADDR-Net uses dual encoders to separately extract features from each image, facilitating explicit feature matching between image pairs. The CPA module, embedded within the encoder, enhances the extraction of global features over long distances. Meanwhile, the MDA module, embedded within the skip connection, filters redundant features and extracts local refined features, thereby improving the U-shaped network architecture. Experimental results demonstrate that ADDR-Net outperforms state-of-the-art methods. Specifically, compared to the Transformer-based TransMorph method, ADDR-Net increases the Dice scores on two publicly available brain datasets, IXI and OASIS, by 1.4% and 3.5%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network architecture

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