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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

2024 lEEE International Conference on Advanced information, Mechanical Engineering, Robotics and Automation, AIMERA 2024

作者： Zheng, Wan Ding, Qingyan Li, Na Pan, Yu Dong, Xuecheng Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350343335

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text;The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy;Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance. © 2024 IEEE.

关键词： Patents and inventions

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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MTRNet: An Efficient Hybrid network Model for Surface Defect Detection of Sheet Metal 36

MTRNet: An Efficient Hybrid Network Model for Surface Defect...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan 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: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep learning

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An adaptive time-convolutional network online prediction method for ocean observation data 36

An adaptive time-convolutional network online prediction met...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Li, Enjing Li, Xiang Zhao, Zhigang Wu, Lu Wang, Yinglong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan 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: (纸本)1891706594

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction. However, there is a’concept drift’ problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios. For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios. We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell. Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams. The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Spatio-temporal data

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Multi-class Bitcoin mixing service identification based on graph classification

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Digital Communications and networks 2024年第6期10卷 1881-1893页

作者： Xiaoyan Hu Meiqun Gui Guang Cheng Ruidong Li Hua Wu School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories for Network and Communication Security Nanjing 211111China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing 211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina Research Base of International Cyberspace Governance Southeast UniversityNanjing 211189China Institute of Science and Engineering Kanazawa UniversityKakumaKanazawa920-1192Japan

Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal *** generally legalize illicit funds by Bitcoin mixing ***,it is critical to investigate the mixing services in cryptocurrency anti-money *** studies treat different mixing services as a class of suspicious Bitcoin ***,they are limited by relying on expert experience or needing to deal with large-scale *** far,multi-class mixing service identification has not been explored *** is challenging since mixing services share a similar procedure,presenting no sharp ***,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain *** paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)***,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical ***,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed ***,it trains a multilayer perceptron classifier to perform classification based on the extracted ***-GC is flexible without handling the full-size network and handcrafting address ***,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for *** experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.

关键词： Bitcoin Anti-money laundering Mixing service Ego network Graph classification

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Length matters:Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

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Digital Communications and networks 2022年第3期8卷 289-302页

作者： Zihan Chen Guang Cheng Ziheng Xu Shuyi Guo Yuyang Zhou Yuyu Zhao School of Cyber Science and Engineering Southeast UniversityNanjing211189China Key Laboratory of Computer Network and Information Integration Ministry of EducationNanjing211189China Purple Mountain Laboratories Nanjing211111China

As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security ***,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a ***,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version *** this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 *** experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and *** to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.

关键词： Encrypted internet traffic Encrypted traffic service classification Multi PDU length sequence Length sensitive composite deep learning TLS-1.3

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Adaptive Group Personalization for Federated Mutual Transfer Learning 41

Adaptive Group Personalization for Federated Mutual Transfer...

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41st International Conference on Machine Learning, ICML 2024

作者： Xu, Haoqing Shen, Dian Wang, Meng Wang, Beilun School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China College of Design and Innovation Tongji University Shanghai China

Mutual transfer learning aims to improve prediction with knowledge from related domains. Recently, federated learning is applied in this field to address the communication and privacy concerns. However, previous clustered federated learning (CFL) solutions lack theoretical guarantee of learnability recovery and require time-consuming hyper-parameter tuning, while centralized mutual transfer learning methods lack adaptability to concept drifts. In this paper, we propose the Adaptive Group Personalization method (AdaGrP) to overcome these challenges. We adaptively decide the recovery threshold with a nonparametric method, adaptive threshold correction, for tuning-free solution with relaxed condition. Theoretical results guarantee the perfect learnability recovery with the corrected threshold. Empirical results show AdaGrP achieves 16.9% average improvement in learnability structure recovery compared with state-of-the-art CFL baselines. Copyright 2024 by the author(s)

关键词： Federated learning

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Practical asynchronous measurement-device-independent quantum key distribution with advantage distillation

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Physical Review A 2024年第2期110卷 022605-022605页

作者： Di Luo Xin Liu Kaibiao Qin Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information

The advantage distillation (AD) method has proven effective in improving the performance of quantum key distribution (QKD). In this paper we introduce the AD method into a recently proposed asynchronous measurement-device-independent (AMDI) QKD protocol, taking finite-key effects into account. Simulation results show that the AD method significantly enhances AMDI QKD, e.g., extending the transmission distance by 16 km with a total pulse count of N=7.24×1013, and enables AMDI QKD, previously unable to generate keys, to generate keys with a misalignment error rate of 10%. As the AD method can be directly integrated into the current system through refined postprocessing, our results facilitate the practical implementation of AMDI QKD in various applications, particularly in scenarios with high channel losses and misalignment errors.

关键词： Quantum cryptography Quantum information processing Quantum information theory

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MAGRET: Machine-generated Text Detection with Rewritten Texts 31

MAGRET: Machine-generated Text Detection with Rewritten Text...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Huang, Yifei Cao, Jingxin Luo, Hanyu Guan, Xin Liu, Bo School of Cyber Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information of Ministry of Education of China Nanjing211189 China School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9798891761964

With the quick advancement in text generation ability of Large Language Model(LLM), concerns about the misuse of machine-generated text(MGT) have grown, raising potential violations of legal and ethical standards. Some existing studies concentrate on detecting machine-generated text in open-source models using in-model features, but their performance on closed-source large models is limited. This limitation occurs because, in the closed-source model detection, the only reference that can be obtained is the texts, which may differ significantly due to random sampling. In this paper, we demonstrate that texts generated by the same model can align both semantically and statistically under similar prompts, facilitating effective detection and traceability. Specifically, we fine-tune a BERT encoder through contrastive learning to achieve semantic alignment in randomly generated texts from the same model. Then, we propose a method called Machine-Generated Text Detection with Rewritten Texts, which designed several prompt refactoring methods and used them to request rewritten text from LLMs. Semantic and statistical relationships between rewritten and original texts provide a basis for detection and traceability. Finally, we expanded the text dataset with multi-parameter random sampling and verified the performance of MAGRET on three text-generated datasets. Experimental results show that previous methods struggle with closed-source model detection, while our approach significantly outperforms baseline methods in this regard. It also shows MAGRET's stable performance in detection and tracing tasks across various randomly sampled texts. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

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