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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

science China(Information sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Lineament extraction and structural mapping using Landsat-9 OLI and Sentinel-1 SAR data in the Proterozoic North Singhbhum Mobile Belt, Eastern India

Geosystems and Geoenvironment

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Geosystems and Geoenvironment 2025年第3期4卷

作者： Choudhury, Nandini Mitra, Atin Kumar Nath, Biswajit Lindsay, Mark D Structural Geology Laboratory Department of Earth Sciences Indian Institute of Engineering Science and Technology West Bengal Shibpur 711103 India Lab of Geoinformatics and Earth Observation Research (LGEOR) Department of Geography and Environmental Studies Faculty of Biological Sciences University of Chittagong Chittagong 4331 Bangladesh CAS-PIFI Visiting Scientist State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute (AIR) Chinese Academy of Sciences (CAS) Olympic Science and Technology Park 20 Datun Road Chaoyang District Beijing 100101 China CSIRO Mineral Resources Kensington 6151 WA Australia School of Earth Sciences The University of Western Australia Crawley 6009 WA Australia ARC Industrial Transformation Training Centre in Data Analytics for Resources and Environment (DARE) Perth and Sydney Australia

The present study explores the application of Landsat-9 OLI and Sentinel-1 SAR data for effective lineament extraction and structural mapping in the Proterozoic North Singhbhum Mobile Belt, Eastern India, an area characterized by complex geological and significant tectonic history. The study employs a combination of manual and automated lineament extraction techniques, with principal component analysis (PCA) applied to Landsat 9 OLI imagery to enhance the visibility of underlying geological structures. The analysis revealed that PC1 captured the highest accuracy for lineament extraction. Sentinel-1 SAR data, acquired in Interferometric Wide (IW) swath mode with dual polarization (VV and VH), was processed using Google Earth Engine, allowing for detailed analysis of surface features. The VH polarization was particularly effective in detecting fine-scale linear features due to its sensitivity to surface roughness and vegetation penetration. Field data collection was integral to the research, allowing for the validation of remote sensing results and the construction of a detailed regional structural map. Stereographic projections were utilized to analyse and visualize structural orientations, including schistosity, lineations, shear foliations and fold axes, providing insights into the area's tectonic evolution. The comparative analysis of lineament characteristics from Landsat-9 OLI and Sentinel-1 SAR datasets indicated that while Sentinel-1 detected a greater number of shorter lineaments, Landsat-9 provided longer lineaments with distinct orientations. This study underscores the efficacy of integrating remote sensing data with field observations and advanced analytical tools, contributing to a deeper understanding of the geological framework. © 2025 The Authors

关键词： Landsat-9 OLI Lineament extraction Sentinel-1 SAR Singhbhum shear zone Stereonet Structural mapping

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Predicting links and their building lime: A path-based approach 30

Predicting links and their building lime: A path-based appro...

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30th AAAI Conference on Artificial Intelligence, AAAI 2016

作者： Li, Manling Jia, Yantao Wang, Yuanzhuo Zhao, Zeya Cheng, Xueqi Institute of Computing Technology Key lab of Network Data Science and Technology CAS Beijing100190 China

ISBN: (纸本)9781577357605

Predicting links and their building time in a knowledge network has been extensively studied in recent years. Most structure-based predictive methods consider structures and the time information of edges separately, which fail to characterize the correlation between them. In this paper, we propose a structure callcd the Time-Difference-Labeled Path, and a link prediction method (TDLP). Experiments show that TDLP outperforms the state-of-the-art methods. Links and Building Time Prediction. © 2016, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Lime

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A Contrastive Pre-training Approach to Discriminative Autoencoder for Dense Retrieval 22

A Contrastive Pre-training Approach to Discriminative Autoen...

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31st ACM International Conference on Information and Knowledge Management, CIKM 2022

作者： Ma, Xinyu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi Cas Key Lab of Network Data Science and Technology Ict Cas University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450392365

Dense retrieval (DR) has shown promising results in information retrieval. In essence, DR requires high-quality text representations to support effective search in the representation space. Recent studies have shown that pre-trained autoencoder-based language models with a weak decoder can provide high-quality text representations, boosting the effectiveness and few-shot ability of DR models. However, even a weak autoregressive decoder has the bypass effect on the encoder. More importantly, the discriminative ability of learned representations may be limited since each token is treated equally important in decoding the input texts. To address the above problems, in this paper, we propose a contrastive pre-training approach to learn a discriminative autoencoder with a lightweight multi-layer perception (MLP) decoder. The basic idea is to generate word distributions of input text in a non-autoregressive fashion and pull the word distributions of two masked versions of one text close while pushing away from others. We theoretically show that our contrastive strategy can suppress the common words and highlight the representative words in decoding, leading to discriminative representations. Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval. © 2022 ACM.

关键词： Decoding

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Density PSO-based software module clustering algorithm

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The Journal of China Universities of Posts and Telecommunications 2018年第4期25卷 38-47页

作者： Sun Jiaze Ling Beilei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution,density PSO-based( DPSO) software module clustering algorithm is proposed. Firstly,the software system is converted into complex network diagram,and then the particle swarm optimization( PSO) algorithm is *** shortest path method is used to initialize the swarm,and the probability selection approach is used to update the particle positions. Furthermore,density-based modularization quality( DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing( HC) algorithm,genetic algorithm( GA),PSO and DPSO algorithm are compared in the modularization quality( MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than the other three traditional algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

关键词： software module clustering complex network PSO MQ modularity density

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LMP-GAN: Out-Of-Distribution Detection For Non-Control data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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Practical decoy-state quantum secure direct communication

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science China(Physics,Mechanics & Astronomy) 2021年第12期64卷 68-75页

作者： Xin Liu Zijian Li Di Luo Chunfeng Huang Di Ma Minming Geng Junwei Wang Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronicsand InformationGuangxi UniversityNanning 530004China CAS Quantum Network Co. LtdShanghai 201315China

Quantum secure direct communication(QSDC) has been demonstrated in both fiber-based and free-space channels using attenuated lasers. Decoy-state QSDC by exploiting four decoy states has been proposed to address the problem of photon-numbersplitting attacks caused by the use of attenuated lasers. In this study, we present an analysis of the practical aspects of decoy-state QSDC. First, we design a two-decoy-state protocol that only requires two decoy states, thereby significantly reducing experimental complexity. Second, we successfully perform full parameter optimization for a real-life QSDC system by introducing a genetic algorithm. Our simulation results show that the two-decoy-state protocol could be the best choice for developing a practical QSDC system. Furthermore, full optimization is crucial for a high-performance QSDC system. Our work serves as a major step toward the further development of practical decoy-state QSDC systems.

关键词： quantum secure direct communication decoy-state method full parameter optimization

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Extended HMM and Ranking models for Chinese Spelling Correction 3

Extended HMM and Ranking models for Chinese Spelling Correct...

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3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2014

作者： Xiong, Jinhua Zhao, Qiao Hou, Jianpeng Wang, Qianbo Wang, Yuanzhuo Cheng, Xueqi Cas Key Laboratory of Network Data Science and Technology Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences China

Spelling correction has been studied for many decades, which can be classified into two categories: (1) regular text spelling correction, (2) query spelling correction. Although the two tasks share many common techniques, they have different concerns. This paper presents our work on the CLP-2014 bake-off. The task focuses on spelling checking on foreigner Chinese essays. Compared to online search query spelling checking task, more complicated techniques can be applied for better performance. Therefore, we proposed a unified framework for Chinese essays spelling correction based on extended HMM and ranker-based models, together with a rule-based model for further polishing. Our system showed better performance on the test dataset. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All rights reserved.

关键词： Statistical tests

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NeuInfer: Knowledge inference on N-ary facts 58

NeuInfer: Knowledge inference on N-ary facts

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Guan, Saiping Jin, Xiaolong Guo, Jiafeng Wang, Yuanzhuo Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781952148255

Knowledge inference on knowledge graph has attracted extensive attention, which aims to find out connotative valid facts in knowledge graph and is very helpful for improving the performance of many downstream applications. However, researchers have mainly poured attention to knowledge inference on binary facts. The studies on n-ary facts are relatively scarcer, although they are also ubiquitous in the real world. Therefore, this paper addresses knowledge inference on n-ary facts. We represent each n-ary fact as a primary triple coupled with a set of its auxiliary descriptive attribute-value pair(s). We further propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. Besides handling the common task to infer an unknown element in a whole fact, NeuInfer can cope with a new type of task, flexible knowledge inference. It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s). Experimental results demonstrate the remarkable superiority of NeuInfer. © 2020 Association for Computational Linguistics

关键词： Knowledge graph

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MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification 29

MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Lear...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhao, Kailin Jin, Xiaolong Guan, Saiping Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attention. Such methods usually consist of a learner as the classifier and a meta learner for specializing the learner to different tasks. For the learner, learning rate is crucial to its performance. However, existing methods treat it as a hyper parameter and adjust it manually, which is time-consuming and laborious. Intuitively, for different tasks and neural network layers, the learning rates should be different and self-adaptive. For the meta learner, it requires a good generalization ability so as to quickly adapt to new tasks. Motivated by these issues, we propose a novel meta learning framework, called MetaSLRCL, for few-shot text classification. Specifically, we present a novel meta learning mechanism to obtain different learning rates for different tasks and neural network layers so as to enable the learner to quickly adapt to new training data. Moreover, we propose a task-oriented curriculum learning mechanism to help the meta learner achieve a better generalization ability by learning from different tasks with increasing difficulties. Extensive experiments on three benchmark datasets demonstrate the effectiveness of MetaSLRCL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Text processing

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