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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Shi, Jiaoli Hu, Chao Yao, Shimao Mei, Zhuolin Wu, Bin Li, Hui Jiujiang Univ. School of Computer and Big Data Jiujiang China Jiangxi Univ. of Finance and Economics School of Software IoT Engineering Nanchang China Jiujiang Key Lab. of Network and Information Security Jiujiang China Jiujiang University School of Computer and Big Data Jiujiang China Peking University School of Elec. and Comp. Engineering Shenzhen China

ISBN: (纸本)9798350330717

Efficiency, security, and flexibility are difficult to balance in a full policy hiding CP-ABE scheme. This paper creatively converts the privacy matching problem of access policy and a user's private key to the privacy three-party set computation problem. Then the new privacy set computation problem is resolved by a message driven key generation method. Additionally, the ROBDD (Reduced Ordered Binary Decision Diagrams) is utilized to rich access structure expression and convert access policies into feasible path sets. Each feasible path is composed of attributes and these attributes are bound to the ciphertext using CP-ABE. Based on the innovative ideas mentioned above, we present a Lighten Fine-grained Access Control scheme supporting Full Policy Hidden (LFAC-FPH) that integrates data confidentiality protection, access policy rich expression but complete hiding, fine-grained access control, and lightweight terminal computing. Our scheme has three advantages: 1) High Efficiency. The proposed scheme can achieve fast privacy matching of access policies during user decryption. 2) Full Policy Hiding. The access policy bound by an owner on the ciphertext does not leak any information. Moreover, a user cannot know attributes they do not own. 3) High Flexibility. The decryption process does not require online from owners or KGC, and system attributes can be added at any time. © 2023 IEEE.

关键词： CP-ABE full policy hiding lighten computation Private-Preserving

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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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Graph Neural Networks with Learnable and Optimal Polynomial Bases

arXiv

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arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is availab.e at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

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A Method of Extracting Discipline Inspection Cases Based on Deep Learning 3

A Method of Extracting Discipline Inspection Cases Based on ...

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3rd International Conference on Education, knowledge and Information Management, ICEKIM 2022

作者： Liu, XiaoLing Chen, JunJie Gao, Jing Fan, Hao Dong, JianMin Inner Mongolia Agricultural University College of Computer and Information Engineering China Inner Mongolia Autonomous Reg. Key Lab. of Large Data Res. and Applic. of Agric. and Anim. Husbandry China Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Hohhot China

ISBN: (纸本)9781665416665

Event extraction is one of the key tasks in information extraction tasks, and it has been widely used in various fields in recent years. In the field of disciplinary inspection and supervision, disciplinary inspection case text has the characteristics of multiple types, multiple professional terms, and strong correlation between event types and event arguments. In the face of huge data, relying on manual analysis by manpower has seriously affected the efficiency of disciplinary inspection. However, there is no corpus in the field of discipline inspection currently availab.e. This article uses BIO annotation to construct a corpus of discipline inspection to lay the foundation for subsequent work. Proposed BERT-BiGRU-CRF event joint extraction model. Use the BERT model to train the discipline inspection corpus, and Combine BiGRU network and CRF network to realize event type recognition and argument extraction. The experimental results show that the model can effectively extract event information in the discipline inspection field. At the same time, in order to better facilitate the work of disciplinary inspection personnel, a disciplinary inspection and supervision event extraction system is constructed to systematically extract all event information contained in the case. © 2022 IEEE.

关键词： Inspection

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Crop Recognition Method Based on Gradient Features and Multilayer Perceptron with Application to Maize Recognition 22

Crop Recognition Method Based on Gradient Features and Multi...

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2nd International Conference on Control and Intelligent Robotics, ICCIR 2022

作者： Xu, Lixing Gao, Jing Chen, Junjie Bai, Yanying Shen, Mingzheng College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Reg. Key Lab. of Big Data Res. and Applic. of Agric. and Animal Husbandry Hohhot China College of Water Conservancy and Civil Engineering Inner Mongolia Agricultural University Hohhot China

ISBN: (纸本)9781450397179

At present, using time-series remote sensing data to identify large-scale crop planting types is of great significance for the government to formulate macro policies and guide agricultural production. As one of the main food crops in Inner Mongolia, it is very necessary to identify maize from large regional crops. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing images and artificial field sampling data, this paper constructs a unique crop temporal vegetation index data set along the Yellow Plain in Inner Mongolia;A multi-layer perceptron algorithm integrating NDVI (Normalized Difference Vegetation Index) gradient features is proposed for the first time to realize the intelligent recognition of corn in the Yellow plain of Inner Mongolia. The experimental results show that the accuracy of the model can reach 85.2%, which is better than the traditional machine learning method using the original NDVI features. © 2022 ACM.

关键词： Time series

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Hybrid Learning Network: A Novel Architecture for Fast Learning

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Procedia Computer Science 2017年 122卷 622-628页

作者： Ying Liu Chao Xiang School of Computer and Engineering University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

There’re many effective architectures of the artificial neural network(ANN). For which the training is a hard work. The cost for training an ANN increases exponentially when the ANN gets deeper or wider. We therefore propose a novel architecture, the Hybrid Learning Network(HLN), to achieve a fast learning with good stablity. The HLN can learn from both lab.led data and unlab.led data at the same time in a hybrid learning manner. It uses a Self Organizing Map unified by the specially designed nonlinear function as the sparsity mask for a hidden layer to improve the training speed. We experiment our architecture on a synthetic dataset to test its regression capability against the traditional architecture, the result is promising.

关键词： hybrid learning neural network sparsity mask self organizing map fast learning

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Deepfake Network Architecture Attribution

arXiv

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arXiv 2022年

作者： Yang, Tianyun Huang, Ziyao Cao, Juan Li, Lei Li, Xirong Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable of handling images generated by seen models with a specific seed, loss and dataset, which is limited in real-world scenarios when fake images may be generated by privately trained models. This motivates us to ask whether it is possible to attribute fake images to the source models’ architectures even if they are finetuned or retrained under different configurations. In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named DNA-Det for this problem. Extensive experiments on multiple cross-test setups and a large-scale dataset demonstrate the effectiveness of DNA-Det. Our source code and dataset can be found here: https://***/ICTMCG/DNA-Det Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Using mobile phone location data for urban activity analysis

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2013 International Workshop on Behavior and Social Informatics and Computing, BSIC 2013

作者： Xie, Rong Xu, Huizheng Yue, Yang International School of Software Wuhan University 430079 Wuhan China Shenzhen Key Laboratory of High Performance Data Mining 518055 Shenzhen China Shenzhen University Shenzhen China State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China

ISBN: (纸本)9783319040479

Mobile phone data record people's calling logs in everyday life, which reflecting their custom, pattern and lifestyle. In this paper, we present approaches to urban activity analysis from real mobile phone location data, including individual activity analysis and group activity analysis, which can be applied to the field of urban planning and management. © Springer International Publishing Switzerland 2013.

关键词： data mining

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knowledge Science, engineering and Management 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Robert Buchmann Claudiu Vasile Kifor Jian Yu

ISBN: (数字)9783319120966

ISBN: (纸本)9783319120959

This book constitutes the refereed proceedings of the 7th International Conference on knowledge Science, engineering and Management, KSEM 2014, held in Sibiu, Romania, in October 2014. The 30 revised full papers presented together with 5 short papers and 3 keynotes were carefully selected and reviewed from 77 submissions. The papers are organized in topical sections on formal semantics; content and document analysis; concept and lexical analysis; clustering and classification; metamodeling and conceptual modeling; enterprise knowledge; knowledge discovery and retrieval; formal knowledge processing; ontology engineering and management; knowledge management; and hybrid knowledge systems.

关键词： Artificial Intelligence data Mining and knowledge Discovery Information Storage and Retrieval Software engineering database Management Information Systems Applications (incl. Internet)

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