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Correction: Self-orthogonal codes over a non-unital ring and combinatorial matrices

Designs, Codes and Cryptography 2023年第2期91卷 691-691页

作者： Shi, Minjia Wang, Shukai Kim, Jon-Lark Solé, Patrick Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Sciences Anhui University Hefei China School of Mathematical Sciences Anhui University Hefei China Department of Mathematics Sogang University Seoul South Korea Aix Marseille Univ CNRS Centrale Marseille I2M Marseilles France

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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification

arXiv

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arXiv 2020年

作者： Xiong, Fu Xiao, Yang Cao, Zhiguo Wang, Yancheng Zhou, Joey Tianyi Wu, Jianxin National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of High Performance Computing A*STAR Singapore Singapore National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Face verification can be regarded as a 2-class fine-grained visual recognition problem. Enhancing the feature’s discriminative power is one of the key problems to improve its performance. Metric learning technology is often applied to address this need, while achieving a good tradeoff between underfitting and overfitting plays the vital role in metric learning. Hence, we propose a novel ensemble cascade metric learning (ECML) mechanism. In particular, hierarchical metric learning is executed in the cascade way to alleviate underfitting. Meanwhile, at each learning level, the features are split into non-overlapping groups. Then, metric learning is executed among the feature groups in the ensemble manner to resist overfitting. Considering the feature distribution characteristics of faces, a robust Mahalanobis metric learning method (RMML) with closed-form solution is additionally proposed. It can avoid the computation failure issue on inverse matrix faced by some well-known metric learning approaches (e.g., KISSME). Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner. Experimental results demonstrate that EC-RMML is superior to state-of-the-art metric learning methods for face verification. And, the proposed ensemble cascade metric learning mechanism is also applicable to other metric learning approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Inverse problems

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Seismic Event Detection via Deep Multi-Task Learning

Seismic Event Detection via Deep Multi-Task Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Yang Yu Lei Zhang Jiakai Shen Qingcai Wang Guiquan Liu School of Computer Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

It is fundamental to detect seismic events reliably and efficiently when processing continuous waveform data recorded by seismic stations. Recently, convolutional neural network (CNN) based detecting methods have been proposed for seismic events detection and obtained great success in this area, where the learning of seismic event detecting network of all seismic stations is considered as one learning task and numerous labeled data need to be collected for training the detecting network. However, they tend to ignore the differences between seismic stations caused by geographic position. Moreover, due to a few seismic activities and high cost of manual data labeling, in some areas, the labeled data for seismic event detecting tasks is insufficient. Under this condition, these methods always encounter over-fitting problem leading to bad detection performance. In this paper, we propose a multi-task based framework based on convolutional neural network for accurate seismic event detection under the condition of insufficient labeled data. Specifically, we first cluster the seismic stations into several station clusters and treat the learning of seismic event detecting network of every station cluster as a learning task, and then we propose a deep multi-task network named detectMTIA among multiple tasks. Experimental results on a real-world seismic dataset with nine stations demonstrate the effectiveness of the proposed framework, especially when the labeled data is insufficient.

关键词： Task analysis Clustering algorithms

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Exploring cascaded write amplification in LSM-tree based key-value stores with solid-state disks 21

Exploring cascaded write amplification in LSM-tree based key...

引用

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Sun, Hui Dai, Shangshang Huang, Jianzhong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Sci. and Tech. Anhui University Hefei China Wuhan National Lab. for Optoelectronics Huazhong University of Sci. and Tech Wuhan China

ISBN: (纸本)9781728120584

Log-structured merge tree (i.e., LSM-tree) based key-value stores, which are widely used in big-data applications, provide high performance. NAND Flash-based Solid state disks (i.e., SSDs) become the popular devices that alternative to hard disk drives (i.e., HDDs) because of the high performance and low power consumption. LSM-tree KV stores with SSDs are deployed in large-scale storage systems to achieve high performance in the cloud. However, the host-side write amplification in LSM-tree KV stores and the device-side write amplification in SSDs can impair the overall system performance. The former that is attributed to compaction has a burn on I/O bandwidth between two sides. The latter, which results from garbage collection in NAND Flash memory, blocks user I/Os from the host and degrades the SSD performance. In this study, we explored cascaded write amplification in LSM-tree KV stores with SSDs. Our primary goal is to comprehensively study two-level cascaded write amplification on the host and device sides. We quantitatively analyze the impact of two-level write amplification on overall performance. This work reveals details of cascaded write amplification and helps developers in the software stacks of LSM-tree based KV stores with SSDs. © 2019 IEEE.

关键词： NAND circuits

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LCD codes from tridiagonal Toeplitz matrice

arXiv

引用

arXiv 2021年

作者： Shi, Minjia Özbudak, Ferruh Xu, Li Solé, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University HefeiAnhui230601 China Department of Mathematics Institute of Applied Mathematics Middle East Technical University Ankara Turkey School of Mathematical Sciences Anhui University HefeiAnhui230601 China I2M Aix Marseille Univ. Centrale Marseille CNRS Marseille France

Double Toeplitz (DT) codes are codes with a generator matrix of the form (I, T) with T a Toeplitz matrix, that is to say constant on the diagonals parallel to the main. When T is tridiagonal and symmetric we determine its spectrum explicitly by using Dickson polynomials, and deduce from there conditions for the code to be LCD. Using a special concatenation process, we construct optimal or quasi-optimal examples of binary and ternary LCD codes from DT codes over extension *** Codes 94B05, 15B05, 12E10 Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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Reversible data hiding in encrypted images based on pixel prediction and bit-plane compression

arXiv

引用

arXiv 2019年

作者： Yin, Zhaoxia Peng, Yinyin Xiang, Youzhi school of Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

Reversible data hiding in encrypted images (RDHEI) receives growing attention because it protects the content of the original image while the embedded data can be accurately extracted and the original image can be reconstructed lossless. To make full use of the correlation of the adjacent pixels, this paper proposes an RDHEI scheme based on pixel prediction and bitplane compression. Firstly, to vacate room for data embedding, the prediction error of the original image is calculated and used for bit-plane rearrangement and compression. Then, the image after vacating room is encrypted by a stream cipher. Finally, the additional data is embedded in the vacated room by multi-LSB substitution. Experimental results show that the embedding capacity of the proposed method outperforms the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Steganography

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Author Correction: Full reconstruction of simplicial complexes from binary contagion and Ising data

引用

Nature communications 2022年第1期13卷 3692页

作者： Huan Wang Chuang Ma Han-Shuang Chen Ying-Cheng Lai Hai-Feng Zhang The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. School of Internet Anhui University Hefei 230601 China. School of Physics and Material Science Anhui University Hefei 230601 China. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 USA. The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei 230601 China. haifengzhang1978@***.

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AutoHR: A strong end-To-end baseline for remote heart rate measurement with neural searching

arXiv

引用

arXiv 2020年

作者： Yu, Zitong Li, Xiaobai Niu, Xuesong Shi, Jingang Zhao, Guoying Center for Machine Vision and Signal Analysis University of Oulu Oulu90014 Finland Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-To-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-To-end networks perform poorly in challenging conditions and establish a strong end-Toend baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method includes three parts: 1) a powerful searched backbone with novel Temporal Difference Convolution (TDC), intending to capture intrinsic rPPGaware clues between frames;2) a hybrid loss function considering constraints from both time and frequency domains;and 3) spatiotemporal data augmentation strategies for better representation learning. Comprehensive experiments are performed on three benchmark datasets to show our superior performance on both intra-and cross-dataset testing. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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An energy efficiency optimization algorithm based on the factor for portion of time division in backhaul-constrained ultra dense hetnets 4

An energy efficiency optimization algorithm based on the fac...

引用

4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Chu, Shiqiang Hu, Yanjun Wang, Yi Ministry of Education Anhui University Key Laboratory of Intelligent Computing Signal Processing Hefei China

ISBN: (纸本)9781538683392

The ultra dense networks (UDN) are considered as a key technology of 5G for its ability to increase communication capacity. However, the problem of constrained backhaul and the lack of energy which is caused by micro base station without power supply equipment are two mainly bottlenecks of UDN. In order to solve the problem of low micro base station's energy efficiency (MIEE) due to constrained backhaul, we propose an energy efficiency optimization algorithm based on the factor for the portion of time division (FPTD). The optimization problem considers a joint design of downlink precoding, power allocation and the FPTD, meanwhile, we utilize first order Taylor convex approximation (FOTCA), convex slack variable and one-dimensional search method (ODS) to solve. Simulation results show that the proposed FPTD-based energy efficiency optimization algorithm (FEEOA) is improved with much better energy efficient compared with the primitive scheme without FPTD, and more throughputs can be transmitted at the same energy consumption. © 2018 IEEE.

关键词： Base stations

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