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External Knowledge-Enhanced Cross-Attention Fusion Model for Tobacco Sentiment Analysis

computers, Materials & Continua 2025年第2期82卷 3381-3397页

作者： Lihua Xie Ni Tang Qing Chen Jun Li Division of Information Center Tobacco Sichuan Industry Co.Ltd.Chengdu610017China Computer Network Information Center Chinese Academy of SciencesBeijing100190China

In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.

关键词： Tobacco sentiment analysis natural language processing cross-attention fusion external knowledge

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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A Large-Scale Study of Abnormal Recursive DNS 20th

A Large-Scale Study of Abnormal Recursive DNS

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20th IFIP WG 10.3 International Conference on network and Parallel Computing, NPC 2024

作者： Hu, Anlei Gong, Liangyi Xie, Jieling Li, Yufu Xie, Gaogang China Internet Network Information Center Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819628636

Recursive DNS plays a pivotal role in safeguarding the security and reliability of users’ access to diverse Internet services. A malfunctioning recursive DNS may impair users’ online experience and pose a grave threat to the security and integrity of the domain name system. Nevertheless, the huge scale and scattered distribution characteristics of recursive DNS make the measurement of abnormal recursive DNS very difficult. In this study, we will take an in-depth look at how top-level domain resolution logs can be used to evaluate and dissect large-scale recursive DNS query patterns. Precisely, we adopt a passive measurement approach, analyzing a massive dataset comprising 1.3 billion resolution logs gathered from. CN top-level domain name servers and also encompassing 110,599 source IPs. Leveraging our profound domain knowledge, we have identified and extracted 12 key features of aberrant recursive DNS query behaviors. Furthermore, we developed a t-SNE-based density clustering model, which has successfully pinpointed 1,417 anomalous recursive DNS servers. Finally, based on our domain expertise and authoritative experts’ insights, we classify these abnormal recursive DNS query behaviors into four categories. Through an in-depth analysis of the intrinsic driving mechanisms of four abnormal behaviors, we provide a comprehensive foundation for recursive DNS safety governance and thus enhance users’ online security. © IFIP International Federation for information Processing 2025.

关键词： network security

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Multiple Edge Data Integrity Verification with Multi-Vendors and Multi-Servers in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4668-4683页

作者： Islam, Md Rashedul Xiang, Yong Uddin, Md Palash Zhao, Yao Kua, Jonathan Gao, Longxiang The School of Information Technology Deakin University GeelongVIC3220 Australia The 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Ensuring Edge Data Integrity (EDI) is imperative in providing reliable and low-latency services in mobile edge computing. Existing EDI schemes typically address single-vendor (App Vendor, AV) single-server (Edge Server, ES), single-vendor multi-server, and multi-vendor multi-server scenarios, which consider a single data replica cached by an ES from the AVs. However, the most practical scenario of Multi-Vendors and Multi-Servers with Multiple Data (MVMS-MD) cached by an ES from different AVs remains unexplored. Current solutions struggle when applied to this scenario due to increased computation and communication costs in the verification process across all ESs using the classical challenge-response per-data multi-round strategy. To tackle this issue, we propose a Multiple EDI-Verification (MEDI-V) approach in this paper. In particular, our MEDI-V utilizes an adaptive Merkle Hash Tree (ad-MHT) to efficiently generate a tree of multiple data replicas within each AV. Next, the dynamic mechanism computes minimal verification information using ad-MHT to create a challenge for individual ESs to produce EDI proofs. The ES then leverages its ad-MHT and the ES’s proof to send the reconstructed ad-MHT root to the AV for verification. Theoretical insights into MEDI-V’s correctness, efficiency, security, and comprehensive evaluations demonstrate its superiority in addressing MEDI issues in the MVMS-MD scenario. © 2002-2012 IEEE.

关键词： Data integrity

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Development of ancient Chinese inscription rubbings knowledge base in the context of cultural communication and art appreciation

Digital Transformation and Society

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Digital Transformation and Society 2025年第1期4卷 73-89页

作者： Qiu, Kaiyu Xia, Cuijuan Liu, QianQian Computer System and Network Center Shanghai Library Shanghai China School of Information Resources Management Renmin University of China Beijing China

Purpose: To promote the cultural communication and art appreciation of Inscriptions and calligraphy which are important cultural heritages in China, with a broad crowd base and educational appreciation value, by using digital humanities methods and technologies, the paper aims to discuss how to develop a new user-oriented knowledge base according to user needs for inscription rubbings research and appreciation. Design/methodology/approach: The paper investigates user needs and current problems on the basis of status quo and summation of extensive service experience;validates the rationality of requirements and user scenarios with user research methods including focus groups, interviews and behavior observations;then designs and develops the technical solution of data process, knowledge base construction and service platform through experimental research methods based on agile project management. Findings: The paper proposes a new reading mode for browsing and appreciation inscription rubbing works, a diverse knowledge-integrated knowledge organizational structure, a systematic user experience and service guiding framework which can be widely applicable to the development of other cultural heritage knowledge bases. Apart from that, the paper puts forward intelligent service development goals for the future. Originality/value: The paper proposes a new reading mode with the overall functions and user experience design and development for browsing and appreciation inscription rubbing works, a diverse knowledge-integrated knowledge organizational structure, a systematic user experience and service guiding framework which can be widely applicable to the development of other cultural heritage knowledge bases. Apart from that, the paper puts forward intelligent service development goals for the future. © 2024, Kaiyu Qiu, Cuijuan Xia and QianQian Liu.

关键词： Ancient Chinese inscription rubbings knowledge base Art appreciation Cultural communication Digital humanities Reading mode User experience design

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LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking 27th

LTNER: Large Language Model Tagging for Named Entity Recogni...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yan, Faren Yu, Peng Chen, Xin Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031784941

The use of LLMs for natural language processing has become a popular trend in the past two years, driven by their formidable capacity for context comprehension and learning, which has inspired a wave of research from academics and industry professionals. However, for certain NLP tasks, such as NER, the performance of LLMs still falls short when compared to supervised learning methods. In our research, we developed a NER processing framework called LTNER (Code is available at https://***/YFR718/LTNER) that incorporates a revolutionary Contextualized Entity Marking Gen Method. By leveraging the cost-effective GPT-3.5 coupled with context learning that does not require additional training, we significantly improved the accuracy of LLMs in handling NER tasks. The F1 score on the CoNLL03 dataset increased from the initial 85.9% to 91.9%, approaching the performance of supervised fine-tuning. This outcome has led to a deeper understanding of the potential of LLMs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Leveraging Cross-Augmentation Consensus and Conflict for Semi-supervised Semantic Segmentation 27th

Leveraging Cross-Augmentation Consensus and Conflict for Se...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Junhao Chen, Junyi Huang, Sibo Zhang, Dongyu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Network and Information Center Huizhou University Huizhou China

ISBN: (纸本)9783031783975

Semi-supervised semantic segmentation leverages both labeled and unlabeled images to accomplish pixel-wise classification task. Within this field, the weak-to-strong consistency regularization has been widely popularized and has become a standard approach. However, unidirectional regularization often leads to the ignorance of correct but filtered predictions and brings the noise of wrong but confident predictions. To address these inherent flaws, we fully leverage Cross-Augmentation Consensus and Conflict (CACC), including Augmentation Feedback Mechanism (AFM) and Category Threshold Controller (CTC). AFM aims to mitigate the influence of incorrect predictions with high-confidence and mine unconfident but accurate predictions by re-weighting the pixel-wise pseudo supervision and applying supplementary regularization. Concurrently, CTC adopts category-specific thresholds by considering the model’s overall performance and the varying category-specific learning difficulty. Experimental results on benchmark datasets demonstrate the superior performance of our method, showcasing its effectiveness in improving semi-supervised semantic segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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DFCRN: Deep-learning-based audio denoising for bird monitoring 5

DFCRN: Deep-learning-based audio denoising for bird monitori...

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5th International Conference on Signal Processing and computer Science, SPCS 2024

作者： Xu, Jinlong Zhang, Huaibin Han, Lin National Supercomputing Center in Zhengzhou Zhengzhou450000 China School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450000 China Information Engineering University Zhengzhou450000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

As people's awareness of ecological protection increases, bird sound monitoring has received more and more attention. Among them, using bird sound monitoring as part of audio recognition has become a hot research topic. Since bird sounds are usually collected in natural environments, they contain a lot of noise, which will affect the monitoring results. To solve this problem, this paper designs a Convolutional Recurrent network (CRN) that enhances feature representation along the frequency axis. This method is based on the Short-time Fourier transform (STFT) features of sound signals, focuses on the complex operation features in the time-frequency domain, and designs an Decode-Encode architecture combined with a time-frequency domain enhancement network to reduce the impact of interference information, We called this network DFCRN. Experimental results on the public datasets Birdsdata and xeno-canto-ca-nv show that compared with other denoising models, the noisy signal after DFCRN enhancement achieves the best results in SegSNR and SI-SNR, and the classification accuracy on xeno-canto-ca-nv is improved by 5%, verifying the effectiveness and robustness of this method. © 2025 SPIE.

关键词： Birds

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Quantum Run-length Encoding: Optimizing Data Compression on Quantum computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jiale Che, Xilong Jin, Shiyong Pan, Kaifan Peng, Shun Hu, Juncheng College of Computer Science and Technology Jilin University Changchun China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (纸本)9798350368741

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE's application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM's superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware. © 2025 IEEE.

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MSCACodec: A Low-Rate Neural Speech Codec With Multi-scale Residual Channel Attention 21st

MSCACodec: A Low-Rate Neural Speech Codec With Multi-scale R...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Yu, Xingye Li, Ye Zhang, Peng Lin, Lingxia Cai, Tianyu Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819601240

In the development of modern communication technology, although wideband speech coding provides high-fidelity speech transmission, its high bandwidth requirement limits its application in resource-constrained environments. Thus, narrowband speech coding is still of great significance. Recently, end-to-end neural speech coding has made significant progress and demonstrated superior compression performance over traditional methods. However, existing methods are limited in reconstructing details, especially in low birate environments. To address this, we introduce MSCACodec, a narrowband-based neural speech codec that achieves advanced performance at low bitrates. MSCACodec adopts a multi-scale residual and channel attention feature fusion method to selectively focus on multi-scale information to enhance feature representation, solving the problem of inconsistent hierarchical information caused by multi-scale feature fusion. In addition, we also propose a Temporal Convolutional Gated Recurrent Unit (TCGRU) module, which combines temporal convolutional networks and gated recurrent units to enhance the reconstruction quality using global context and gating mechanisms. The experimental results show that, whether in subjective or objective evaluation, MSCACodec achieves higher quality reconstructed speech than Encodec and HiFiCodec at bitrates of 1.2 kbps and 2.4 kbps, and is even better than LyraV2 and Opus at 6 kbps. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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