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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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Generative Retrieval for Book Search 25

Generative Retrieval for Book Search

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and data Mining V.1

作者： Yubao Tang Ruqing Zhang Jiafeng Guo Maarten de Rijke Shihao Liu Shuaiqiang Wang Dawei Yin Xueqi Cheng CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China University of the Chinese Academy of Sciences Beijing China & University of Amsterdam Amsterdam Netherlands CAS Key Lab of Network Data Science and Technology ICT CAS Beijing China & University of Chinese Academy of Sciences Beijing China University of Amsterdam Amsterdam Netherlands Baidu Inc. Beijing China

ISBN: (纸本)9798400712456

In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, where the outline provides hierarchical information between chapters and sections. Generative retrieval (GR) is a new retrieval paradigm that consolidates corpus information into a single model to generate identifiers of documents that are relevant to a given query. How can GR be applied to book search? Directly applying GR to book search is a challenge due to the unique characteristics of book search: The model needs to retain the complex, multi-faceted information of the book, which increases the demand for labeled data. Splitting book information and treating it as a collection of separate segments for learning might result in a loss of hierarchical *** propose an effective Generative retrieval framework for Book Search (GBS) that features two main components: data augmentation and outline-oriented book encoding. For data augmentation, GBS constructs multiple query-book pairs for training; it constructs multiple book identifiers based on the outline, various forms of book contents, and simulates real book retrieval scenarios with varied pseudo-queries. This includes coverage-promoting book identifier augmentation, allowing the model to learn to index effectively, and diversity-enhanced query augmentation, allowing the model to learn to retrieve effectively. Outline-oriented book encoding improves length extrapolation through bi-level positional encoding and retentive attention mechanisms to maintain context over long sequences. Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines, achieving a 9.8% improvement in terms of MRR@20, over the state-of-the-art RIPOR method. Experiments on public datasets confirm the robustness and generalizability of GBS, highlighting its potential to enhance book retrieval.

关键词： book retrieval

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Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution

arXiv

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arXiv 2025年

作者： Zhu, Jizhao Shi, Akang Li, Zixuan Bai, Long Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science Shenyang Aerospace University Shenyang China

In this paper, we aim to enhance the robustness of Universal Information Extraction (UIE) by introducing a new benchmark dataset, a comprehensive evaluation, and a feasible solution. Existing robust benchmark datasets have two key limitations: 1) They generate only a limited range of perturbations for a single Information Extraction (IE) task, which fails to evaluate the robustness of UIE models effectively;2) They rely on small models or handcrafted rules to generate perturbations, often resulting in unnatural adversarial examples. Considering the powerful generation capabilities of Large Language Models (LLMs), we introduce a new benchmark dataset for Robust UIE, called RUIE-Bench, which utilizes LLMs to generate more diverse and realistic perturbations across different IE tasks. Based on this dataset, we comprehensively evaluate existing UIE models and reveal that both LLM-based models and other models suffer from significant performance drops. To improve robustness and reduce training costs, we propose a data-augmentation solution that dynamically selects hard samples for iterative training based on the model’s inference loss. Experimental results show that training with only 15% of the data leads to an average 7.5% relative performance improvement across three IE tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Information retrieval

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Separate Source Channel Coding Is Still What You Need:An LLM-Based Rethinking

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ZTE Communications 2025年第1期23卷 30-44页

作者： REN Tianqi LI Rongpeng ZHAO Mingmin CHEN Xianfu LIU Guangyi YANG Yang ZHAO Zhifeng ZHANG Honggang College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Shenzhen CyberAray Network Technology Co. Ltd.Shenzhen 518000China China Mobile Research Institute Beijing 100053China The Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511453China Zhejiang Lab Hangzhou 311121China Faculty of Data Science City University of MacaoMacao 999078China

Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ***,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for *** demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over *** proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point *** comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication *** other words,separate source channel coding is still what we need.

关键词： separate source channel coding(SSCC) joint source channel coding(JSCC) end-to-end communication system Large Language Model(LLM) lossless text compression Error Correction Code Transformer(ECCT)

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TransMatch: Employing Bridging Strategy to Overcome Large Deformation for Feature Matching in Gastroscopy Scenario

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zhu, Guosong Qin, Zhen Yu, Linfang Ding, Yi Qin, Zhiguang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu611731 China Industrial Research Institute Chengdu610000 China

Feature matching is widely applied in the image processing field. However, both traditional feature matching methods and previous deep learning-based methods struggle to accurately match the features with severe deformations and large displacements, particularly in gastroscopy scenario. To fill this gap, an effective feature matching framework named TransMatch is proposed, which addresses the largely displacements issue by matching features with global information leveraged via Transformer structure. To address the severe deformation of features, an effective bridging strategy with a novel bidirectional quadratic interpolation network is employed. This bridging strategy decomposes and simplifies the matching of features undergoing severe deformations. A deblurring module for gastroscopy scenario is specifically designed to address the potential blurriness. Experiments have illustrated that proposed method achieves state-of-the-art performance of feature matching and frame interpolation in gastroscopy scenario. Moreover, a large-scale gastroscopy dataset is also constructed for multiple tasks. © 1982-2012 IEEE.

关键词： Feature extraction

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guoliang Li Ruiqi Zhang Zhe Sun Lingkai Xing Yu Zhang College of Cyber Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China College of Computer Science Nankai University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset’s quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR.

关键词： Deep learning Computer vision Heavily-tailed distribution Codes Accuracy Noise Speech recognition Acoustics Noise measurement Speech processing

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AMTrans:Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1329-1341页

作者： Lei Gao Liyuan Cui Shuwen Chen Lizhen Deng Xiaokang Wang Xiaohong Yan Hu Zhu College of Electronic and Optical Engineering & College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer Science and Technology Hainan UniversityHaikou 570228China

Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry *** improve the quality and reliability of infrared spectroscopy signals,deconvolution is a crucial preprocessing *** by the transformer model,we propose an Auto-correlation Multi-head attention Transformer(AMTrans)for infrared spectrum sequence *** auto-correlation attention model improves the scaled dot-product attention in the *** utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation *** auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the *** proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic *** comparing the experiments with other deconvolution techniques,the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.

关键词： spectroscopy spectral deconvolution transformer auto-correlation mechanism

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Understanding User Passwords through Parsing Tree

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Ding Shan, Xuan Wu, Yuxuan Jia, Chunfu Nankai University College of Cryptology and Cyber Science Tianjin300350 China Ministry of Education of China Henan Key Laboratory of Network Cryptography Key Laboratory of Cyberspace Security China Nankai University Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

Passwords are today's dominant form of authentication, and password guessing is the most effective method for evaluating password strength. Most password guessing models (e.g., PCFG, Markov, and RFGuess) regard passwords as sequences composed of basic units (i.e., characters/segments), with the information flow being unidirectional (i.e., predicting the next unit based on the preceding units). However, modeling passwords in a single direction fails to capture users' password creation behavior that is actually impacted by the full password context. Through an in-depth analysis of real-world passwords, we reveal that users often create passwords around a central keyword, like common words, names, or dates, and then embellish them with numbers or symbols. Based on this observation, we, for the first time, attempt to parse passwords as trees. Unlike existing sequence models, trees can reveal the semantic connections within passwords and the logical thought processes users follow when creating passwords. For instance, in the password iloveyou, the basic units i and you are semantically dependent on the predicate love, forming a natural tree structure. We propose a trawling guessing model called PassTree and a targeted guessing model based on personally identifiable information (PII), named PassTree-PII. Our extensive experiments demonstrate the effectiveness of our models: (1) PassTree outperforms its leading counterparts by 0.38%-2.51% when guessing numbers are below 107 (2) PassTree-PII achieves a cracking rate comparable to the state-of-the-art RFGuess-PII proposed in USENIX Security'23, but operates significantly more efficiently, using only 0.87% of the memory and being 16.60 times faster. Our work provides a new perspective on understanding user passwords and demonstrates a feasible technical route of applying tree structures to password guessing. © 2025 IEEE.

关键词： Markov processes

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Riemannian Complex Hermit Positive Definite Convolution network for Polarimetric SAR Image Classification

arXiv

引用

arXiv 2025年

作者： Shi, Junfei Nie, Mengmeng Li, Yuke Jin, Haiyan Lin, Weisi Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China College of Computing and Data Science Nanyang Technological University 639798 Singapore

Deep learning can learn high-level semantic features in Euclidean space effectively for PolSAR images, while they need to covert the complex covariance matrix into a feature vector or complex-valued vector as the network input. However, the complex covariance matrices are essentially a complex Hermit positive definite (HPD) matrix endowed in Riemannian manifold rather than Euclidean space. The matrix’s real and imagery parts are with the same significance, as the imagery part represents the phase information. The matrix vectorization will destroy the geometric structure and manifold characteristics of complex covariance matrices. To learn complex HPD matrices directly, we propose a Riemannian complex HPD convolution network(HPD CNN) for PolSAR images. This method consists of a complex HPD unfolding network(HPDnet) and a CV-3DCNN enhanced network. The proposed complex HPDnet defines the HPD mapping, rectifying and the logEig layers to learn geometric features of complex matrices. In addition, a fast eigenvalue decomposition method is designed to reduce computation burden. Finally, a Riemannian-to-Euclidean enhanced network is defined to enhance contextual information for classification. Experimental results on two real PolSSAR datasets demonstrate the proposed method can achieve superior performance than the state-of-the-art methods especially in heterogeneous regions. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication network

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