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2018 International Conference on Network, Communication, computer Engineering (NCCE 2018)

作者： Wanli Lyu Jiahao Deng Yu Zhang Dongshuo Yin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

In this paper,our proposed scheme using two steganography *** other data hiding schemes,this scheme studies the characteristics of the Tangram puzzle model and the secret message bytes that make up the eight binary bits that are hidden into the two *** matrix M *,horizontal and vertical coordinate range from 0 to 255,and the pixel pairs in the cover image are used to help hide the secret message bytes in our *** paired pixel points into single and double number hidden in two *** show that the program can hide relatively more pixels in a cover image with less losing image quality.

关键词： steganography images Tangram puzzle matrix M hide relatively scenario

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A Reliable Data Hiding Scheme Using Jigsaw Sudoku

A Reliable Data Hiding Scheme Using Jigsaw Sudoku

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2018 International Conference on Network, Communication, computer Engineering (NCCE 2018)

作者： Wanli Lyu Dongshuo Yin Yu Zhang Jiahao Deng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

A distinctive data hiding method based on Jigsaw Sudoku is proposed in this *** from other methods,the proposed method is based on Jigsaw Sudoku which is a kind of irregularly shaped *** to the novelty of Jigsaw Sudoku,the proposed method can provide a safer scheme for data hiding than that with pure *** proposed method can hide a certain number of 9-ary numbers in a steganographic image which has a corresponding *** a 256*256 key matrix stretched from a size 9*9 Jigsaw Sudoku,secret message can be hidden in a vector image byte by byte in the form of *** results show that,the proposed method has a better peaksignal-to-noise-ratio(PSNR) compared with some other data hiding methods.

关键词： Sudoku PSNR hiding methods irregularly shaped steganographic image corresponding specification

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Unsupervised adversarial domain adaptation for acoustic scene classification

arXiv

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arXiv 2018年

作者： Gharib, Shayan Drossos, Konstantinos Çakir, Emre Serdyuk, Dmitriy Virtanen, Tuomas Audio Research Group Lab. of Signal Processing Tampere University of Technology Tampere Finland Montreal Institute for Learning Algorithms MontrealQC Canada

A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. As a countermeasure, we present the first method of unsupervised adversarial domain adaptation for acoustic scene classification. We employ a model pre-trained on data from one set of conditions and by using data from other set of conditions, we adapt the model in order that its output cannot be used for classifying the set of conditions that input data belong to. We use a freely availab.e dataset from the DCASE 2018 challenge Task 1, subtask B, that contains data from mismatched recording devices. We consider the scenario where the annotations are availab.e for the data recorded from one device, but not for the rest. Our results show that with our model agnostic method we can achieve ∼ 10% increase at the accuracy on an unseen and unlab.led dataset, while keeping almost the same performance on the lab.led dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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AIM 2019 challenge on real-world image super-resolution: Methods and results

arXiv

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arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailab.e. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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Force-Position Control for Upper Limb Exoskeleton Based on Muscle Model with Compensation

Force-Position Control for Upper Limb Exoskeleton Based on M...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： Min Lin Haoping Wang Yang Tian Sino-French Int. Joint Lab. Automatic Control and Signal Processing (LaFCAS) school of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781538679814

This paper investigates a force-position controller using neural network model free control with time-delay estimation (TDE-MFNNC) for the motion of the upper limb exoskeleton. The system establishes the muscle force prediction model based on the scaling principle of Hill model to estimate the torque provided by the upper limb. Then the upper limb exoskeleton is built in Solidworks as a virtual prototype which is used to instead the real exoskeleton model. We performed control stability to verify the effectiveness of the controller by comparing with TDE-iPD controller and with compensation the simulation result has a more satisfactory tracking performance.

关键词： TDE-MFNNC motion scaling principle Upper Extremity shelling compensated control stability Position control Controllers Virtual prototyping Simulation results control period

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Sparse weighted naive bayes classifier for efficient classification of categorical data 3

Sparse weighted naive bayes classifier for efficient classif...

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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Zheng, Zhuoyuan Cai, Yunpeng Yang, Yujie Li, Ye Guangxi Key Laboratory of Trusted Software Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin541004 China Shenzhen Institutes of Advanced Technology Key Laboratory for Biomedical Informatics and Health Engineering Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781538642108

Feature selection has become a key challenge in machine learning with the rapid growth of data size in real-world applications. However, existing feature selection methods mainly focus on numeric data, which will lead to quality loss when handling classification problems involving categorical variables. In this paper, we proposed an improvement of Bayesian Classifier with the sparse regression technology. To the best of our knowledge, this is the first attempt to extend sparse regression for directly process of categorical variables. We implemented the idea for the case of weighted naive Bayes classifier. The introduction of L1 regularized learning ensures the algorithm to retain only a minimal subset of variables for model building, while at the same time achieves a near-optimal decision hyper-plane, which leads to excellent performance in case of high dimensional or small sample size situations. We carried out benchmark test on five UCI benchmark categorical data sets, which proved that the proposed algorithm have competitive performances over the original weighted naive bayes classifier and several state-of-the-art feature selection methods including L1 logistic regression and SVM-RFE. © 2018 IEEE.

关键词： Feature Selection

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A new multilevel reversible bit-planes data hiding technique based on histogram shifting of efficient compressed domain

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Vietnam Journal of computer Science 2018年第2期5卷 185-196页

作者： Rashid Abbasi Bin Luo Gohar Rehman Haseeb Hassan Muhammad Shahid Iqbal Lixiang Xu School of Computer and Technology Anhui University Hefei People’s Republic of China Key Lab of Intelligent Computing and Signal Processing of MOE and School of Computer and Technology Anhui University Hefei People’s Republic of China College of Computer Science Technology Chongqing University Chongqing People’s Republic of China

In this paper, we proposed a new technique for reversible data hiding based on efficient compressed domain with multiple bit planes. We conducted a sequence of experiments to use block division scheme to appraise the result with different parameters and amended the probability of zero point in every block of histogram. This scheme attained more embedding capacity and high-quality of stego-image. Experimental consequences prove that the proposed method effectively achieved the objective of high embedding capacity and sustaining the quality of image.

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Learning deep mixtures of Gaussian process experts using sum-product networks

arXiv

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arXiv 2018年

作者： Trapp, Martin Peharz, Robert Rasmussen, Carl E. Pernkopf, Franz Signal Processing and Speech Communication Lab. Graz University of Technology Graz Austria Austrian Research Institute for Artificial Intelligence Vienna Austria Computational and Biological Learning Lab. University of Cambridge Cambridge United Kingdom

While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in memory. In this paper, we introduce a natural and expressive way to tackle these problems, by incorporating GPs in sum-product networks (SPNs), a recently proposed tractable probabilistic model allowing exact and efficient inference. In particular, by using GPs as leaves of an SPN we obtain a novel flexible prior over functions, which implicitly represents an exponentially large mixture of local GPs. Exact and efficient posterior inference in this model can be done in a natural interplay of the inference mechanisms in GPs and SPNs. Thereby, each GP is - similarly as in a mixture of experts approach - responsible only for a subset of data points, which effectively reduces inference cost in a divide and conquer fashion. We show that integrating GPs into the SPN framework leads to a promising probabilistic regression model which is: (1) computational and memory efficient, (2) allows efficient and exact posterior inference, (3) is flexible enough to mix different kernel functions, and (4) naturally accounts for non-stationarities in time series. In a variate of experiments, we show that the SPN-GP model can learn input dependent parameters and hyper-parameters and is on par with or outperforms the traditional GPs as well as state of the art approximations on real-world data. Copyright © 2018, The Authors. All rights reserved.

关键词： Gaussian distribution

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience lab.ratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the availab.e imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval

Fast Semantic Preserving Hashing for Large-Scale Cross-Modal...

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IEEE International Conference on Data Mining (ICDM)

作者： Xingzhi Wang Xin Liu Shujuan Peng Yiu-ming Cheung Zhikai Hu Nannan Wang Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China State Key Lab. of Integrated Services Networks & School of Telecommun. Eng. Xidian University Xi’an China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Dept. of Comput. Sci. and Institute of Research and Continuing Education HK Baptist University Hong Kong SAR China

Most Cross-modal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving time-consuming training procedures for large-scale dataset. To tackle these issues, we first formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to large-scale cross-modal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class lab.ls, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modality-specific projection function learning and an efficient closed-form solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the state-of-the-arts.

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