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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...

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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

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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

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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

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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

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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...

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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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Bernstein Bezout Matrix with Applications to the Generalized Lienard-Chipart Stability Criterion

Bernstein Bezout Matrix with Applications to the Generalized...

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第三十八届中国控制会议

作者： Huazhang Wu Xiaoting Shen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Mathematical Sciences Anhui University

In polynomial and linear control systems, the Lienard-Chipart stability criterion plays an important role in the judgment of the zeros of a real polynomial based on the inertia of a Bezout matrix. In this paper we consider the case in the Bernstein polynomials basis. First, the Bernstein Bezout matrix and some important properties are introduced, and then a generalized perturbations of a real polynomial under the Bernstein polynomials basis is considered. Finally, a generalized Lienard-Chipart stability criterion in terms of the Bernstein Bezout matrix is established.

关键词： Bernstein polynomials basis Bernstein Bezout matrix perturbation Lienard-Chipart stability criterion

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Flexible and Low Profile Antenna with AMC for Wireless Body Area Network Applications

Flexible and Low Profile Antenna with AMC for Wireless Body ...

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Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL)

作者： Kaifeng Cui Pingjuan Zhang Dongya Cheng Chunyang Jin Jundui Mu Jun Wang Minquan Li Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China Anhui Science And Technology University China

ISBN: (数字)9781728153049

ISBN: (纸本)9781728153056

This letter presents a flexible antenna with artificial magnetic conductor (AMC). A CPW antenna is designed because its broadband characteristics with the relative bandwidth of 39.7% and 13.0% respectively. The proposed antenna operable at 2.45/5.8 GHz for wireless body area network applications and PDMS substrate is used makes the antenna thin and bendable. The AMC surface is integrated with the antenna to improve its radiation performance and to reduce the specific absorption rate (SAR). The 2 × 4 unit structure of AMC ensures a low profile with a thickness and a small size with the area of 50 * 100 mm 2 . The simulate result shows an AMC reflector increases the front-to-back ratio and antenna gain in free space. Moreover, the performance of the antenna with and without the AMC reflector under different crumpling conditions has been studied and presented, and the result shows the antenna performance is observed to be very robust against the effect of bending. The proposed wearable antenna structure can be used for wireless body area network (WBAN) wireless devices.

关键词： Wireless communication Electromagnetics Body area networks Broadband antennas Loaded antennas Photonics

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On the Detection of Shilling Attacks in Federated Collaborative Filtering

On the Detection of Shilling Attacks in Federated Collaborat...

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Reliable Distributed Systems

作者： Yangfan Jiang Yipeng Zhou Di Wu Chao Li Yan Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University) Ministry of Education China Macquarie University Sydney NSW Australia Tencent Inc Shenzhen China

ISBN: (数字)9781728176260

ISBN: (纸本)9781728176277

Federated collaborative filtering (Fed-CF) is a variant of federated learning (FL) models, which can protect user privacy in recommender systems. In Fed-CF, the recommendation model is collectively trained across multiple decentralized clients by exchanging gradients only. However, the decentralized nature of Fed-CF makes it vulnerable to shilling attacks, which can be realized by inserting fake ratings of target items to distort recommendation results. Unfortunately, previous detection algorithms cannot work well in the FL framework, as all original data samples are not disclosed at all. In this paper, we are the first to systematically study the problem of shilling attacks in the context of federated learning, and propose an effective detection method called Federated Shilling Attack Detector (FSAD) to detect shilling attackers in Fed-CF. We first show the feasibility of shilling attacks in Fed-CF. Next, we dedicatedly design four novel features based on exchanged gradients among clients. By incorporating these gradient-based features, we train a semi-supervised Bayes classifier to identify shilling attackers effectively. Finally, we conduct extensive experiments based on real-world datasets to evaluate the performance of our proposed FSAD method. The experimental results show that FSAD can detect shilling attackers in Fed-CF with high accuracy, with the F 1 value as high as 0.90 on the Netflix dataset, which approaches the performance of the optimal detector that utilizes complete private user information for detection.

关键词： Privacy Collaborative filtering Detectors Collaborative work Information filters Reliability Recommender systems

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