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14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Zhu, Yujia Wang, Ruoxi Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

With the popularity of neural networks, more and more service providers are providing us with convenient neural network services. In this situation, it is necessary for service providers to grant hierarchical authorization of the networks to meet the service requirements of different users. Pruning technology, as a mainstream method for modifying neural network structures, can also be applied in the hierarchical authorization of neural networks. This article uses the method of polarization pruning to decentralize and grade neural networks. Firstly, we extract the scale factors of the batch normalization(BN) layer as a measure of the importance of each channel. Then, sparse methods are used to screen for scale factors. In order to make the selected scale factors have more obvious and reasonable truncation intervals, we also use polarization methods to induce clustering of scale factors. According to different grading requirements, we prune the channels corresponding to different quantities of scale factors. The experiment proves that our method has good grading performance on different datasets and different neural networks. Moreover, good grading results can be achieved by pruning only a small portion of the channels. © 2024 IEEE.

关键词： grading hierarchical authorization pruning

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Internet Public Safety Event Grading and Hybrid Storage Based on Multi-feature Fusion for Social Media Texts 28th

Internet Public Safety Event Grading and Hybrid Storage Bas...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Hu, Die Xie, Yulai Feng, Dan Zhao, Shixun Fu, Pengyu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some events that cause negative public opinion seriously threaten public security, which are called Internet Public Safety events (IPSe). Existing solutions use a single metric to realize event detection, which has a high false detection rate for Internet Public Safety events. In addition, they lack countermeasures after the outbreak of public opinion, resulting in inefficient information management. This paper proposes a novel Internet Public Safety event grading method based on multi-feature, which measures events from the three perspectives of heat, emotion, and sensitivity. In order to improve the retrieval efficiency of events information, we implement a smart dynamic hot/cold data migration mechanism using a hybrid storage system containing solid-state drives and hard-disk drives, which realizes real-time data adjustment in the storage layer and ensures efficient events management. In the experiments, we verify our method through several real internet events, and the results show that our method achieves the state-of-the-art accuracy in the detection of Internet Public Safety events and realizes efficient retrieval with a low query overhead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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Instance by Instance: An Iterative Framework for Multi-Instance 3D Registration

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1117-1128页

作者： Yang, Jiaqi Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Quan, Siwen School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China School of Electronic and Control Engineering Chang'an University Xi'an710064 China Shenzhen University MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen518060 China

Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. © 2014 Chinese Association of Automation.

关键词： 3D modeling

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Invisible Watermark and Hypothesis Testing for Multi-Ownership Verification Framework of Deep Neural Network 14

Invisible Watermark and Hypothesis Testing for Multi-Ownersh...

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14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Wang, Ruoxi Zhu, Yujia Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

With the rapid development of information technology, artificial intelligence models have become the core asset of tech giants, highlighting the importance of copyright protection. Black-box watermarking has gained attention due to its ability to verify the legality of models remotely without relying on internal model information. The challenges lie in embedding and verifying watermarks without accessing internal model parameters, while ensuring the robustness and stealth of the watermark. This paper focuses on discussing effective ways to protect the copyright of black-box watermarking models and detect and prevent unauthorized user behavior that infringes on model ownership. Therefore, this paper proposes a multi-ownership verification framework based on invisible watermark and hypothesis testing, which mainly consists of two parts: watermark generation and copyright verification. The watermark generation uses a dynamic watermarking method to generate invisible backdoor watermark and verifies copyright through hypothesis testing. This framework has high assurance and effectiveness under various models of CIFAR-10 and MNIST datasets and shows strong robustness in VGG and Resnet model pruning attacks. This research provides new ideas and methods for the protection of model copyrights in deep neural networks. © 2024 IEEE.

关键词： digital watermarking hypothesis testing invisible watermark neural networks copyright protection ownership verification

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Single inclusive hadron production at the LHC with an improved impact parameter dependent saturation model

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Chinese Physics C 2023年第6期47卷 89-98页

作者：弓晓锋蔡燕兵周代翠向文昌 Guizhou Science and Technology Information Center Guiyang 550002China Guizhou Key Laboratory of Big Data Statistic Analysis and Guizhou Key Laboratory in Physics and Related AreasGuizhou University of Finance and EconomicsGuiyang 550025China Key Laboratory of Quark and Lepton Physics(MOE) and Institute of Particle PhysicsCentral China Normal UniversityWuhan 430079China Physics Division Guangzhou Maritime UniversityGuangzhou 510725China

To obtain a reasonable description of the hadron production at the LHC energies,the impact parameter dependent saturation model is modified by inclusion of an anomalous dimensionγ,which controls the slope of the scattering amplitude in the transition from the dilute region to the saturation *** calculate the transverse momentum distribution and nuclear modification factor of theπ^(0)and charged hadrons with the improved model,and the results are consistent with measurements performed at the ***,we use the original impact parameter dependent model to study the aforementioned measurements performed at the LHC by adjusting its *** find that the improved model is more consistent with the experimental data than the original one,as the anomalous dimension plays a significant role in the suppression of the evolution of the scattering amplitude.

关键词： gluon saturation high energy heavy ion collisions small-x physics

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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An Extended-Isomap for high-dimensional data accuracy and efficiency: a comprehensive survey

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Multimedia Tools and Applications 2024年第38期83卷 85523-85574页

作者： Yousaf, Mahwish Shakoor Khan, Muhammad Saadat Ullah, Shamsher Institute of Intelligence Machine Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei230027 China Key Laboratory of Materials Physics Institute of Solid-State Physics Chinese Academy of Sciences Anhui Hefei230027 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China

Manifold learning is a widely adopted nonlinear dimensionality reduction technique employed to discover low-dimensional representations from high-dimensional data and to explore the intrinsic data structure. It encompasses a range of nonlinear techniques, including Isomap, Local Linear Embedding (LLE), Local Tangent Space Alignment (LTSA), and Multidimensional Scaling (MDS), among others. Isomap, as a popular method in manifold learning, has exhibited promising results. However, it encounters several challenges, such as the Topological Stability Problem, Shortest Path Distance, and noise sensitivity, which compromise its accuracy and elevate its computational demands. This survey paper presents three novel existing approaches to address these challenges and enhance the performance of Isomap. Firstly, the FastIsomap method, an existing approach that combines state-of-the-art algorithms like the KD tree and NN-descent to increase graph accuracy while reducing computational cost. Secondly, the FastIsomapVis method, also an existing technique, employs a hierarchical divide-and-conquer strategy employing the KD tree and Dijkstra Buckets Double, which enhances accuracy and reduces computational time in Isomap. Lastly, the NR-Isomap method, another existing approach, used the Local Tangent Space Alignment algorithm to remove noise sensitivity and short-circuit edge problems. The existing methods improve the accuracy of Isomap but also streamline its computational requirements. Furthermore, this survey paper discusses the open issues and future research directions for Isomap and manifold learning methods. It can be a valuable resource for researchers seeking insights across diverse domains. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE OPTIMIZATION FOR MAXIMUM MEAN DISCREPANCY 12

DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Shuhai Song, Yiliao Yang, Jiahao Li, Yuanqing Han, Bo Tan, Mingkui South China University of Technology China Pazhou Laboratory China The University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong

Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues, misleading information, or hallucination issues. Therefore, it is very urgent and important to detect MGTs in many situations. Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs. In this paper, we seek to exploit maximum mean discrepancy (MMD) to address this issue in the sense that MMD can well identify distributional discrepancies. However, directly training a detector with MMD using diverse MGTs will incur a significantly increased variance of MMD since MGTs may contain multiple text populations due to various LLMs. This will severely impair MMD's ability to measure the difference between two samples. To tackle this, we propose a novel multi-population aware optimization method for MMD called MMD-MP, which can avoid variance increases and thus improve the stability to measure the distributional discrepancy. Relying on MMD-MP, we develop two methods for paragraph-based and sentence-based detection, respectively. Extensive experiments on various LLMs, e.g., GPT2 and ChatGPT, show superior detection performance of our MMD-MP. The source code is available at https://***/ZSHsh98/MMD-MP. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Multi-objective multi-population Genetic Programming for feature selection and classification to high-dimensional data

Multi-objective multi-population Genetic Programming for fea...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Li, Qiaoman Gao, Xiaoying Meng, Wenyang Ma, Jianbin Hebei Agricultural University College of Information Science and Technology Baoding China Victoria University of Wellington School of Engineering and Computer Science Wellington New Zealand College of Information Science and Technology Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University Baoding China

ISBN: (纸本)9798400704956

Classification for high-dimensional data is a challenging task due to a great number of redundant and irrelevant features. Genetic Programming (GP) has its built-in feature selection characteristics and is suitable for processing high-dimensional data. However, if the number of features in GP individual is not restricted, the bloat phenomenon will occur, which affects the generalization of the training model. Moreover, GP is easy to fall into local search in the evolutionary process. In this paper, a multi-objective multi-population GP classifier construction method is proposed, which uses a multipopulation co-evolution strategy to divide the evolving population into the main and auxiliary populations with different evolutionary strategies and different evaluation criteria, and employs a multiobjective strategy to ensure classification performance and restrict the number of features at the same time. Experiments on seven high-dimensional datasets show that our proposed multi-population co-evolution strategy and multi-objective strategy are all effective to improve the classification performance of GP classifiers. Comparisons with three state-of-the-art GP classifier construction methods show that our proposed methods achieve better or comparable classification performance on most cases. © 2024 held by the owner/author(s).

关键词： Spatio-temporal data

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