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Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

computers, Materials & Continua 2018年第6期55卷 495-507页

作者： Yang Du Zhaoxia Yin Xinpeng Zhang Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei230601P.R.China Department of Computer Science Purdue UniversityWest lafayette47906USA School of Communication and Information Engineering Shanghai UniversityShanghai 200072P.R.China

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram *** most in JPEG bitstream consists of a sequence of VLCs(variable length codes)and the appended *** VLC has a corresponding RLV(run/length value)to record the AC/DC *** achieve lossless data hiding with high payload,we shift the histogram of VLCs and modify the DHT segment to embed *** we sort the histogram of VLCs in descending order,the filesize expansion is *** paper’s key contribution includes:Lossless data hiding,less filesize expansion in identical pay-load and higher embedding efficiency.

关键词： Lossless data hiding histogram VLC JPEG.

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Reduction theorem for secrecy over linear network code for active attacks

arXiv

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arXiv 2020年

作者： Hayashi, Masahito Owari, Masaki Kato, Go Cai, Ning Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518000 China Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117542 Singapore Department of Computer Science Faculty of Informatics Shizuoka University Japan NTT Communication Science Laboratories NTT Corporation Japan School of Information Science and Technology ShanghaiTech University Middle Huaxia Road no 393 Pudong Shanghai201210 China

We discuss the effect of sequential error injection on information leakage under a network code. We formulate a network code for the single transmission setting and the multiple transmission setting. Under this formulation, we show that the eavesdropper cannot improve the power of eavesdropping by sequential error injection when the operations in the network are linear *** demonstrate the usefulness of this reduction theorem by applying a concrete example of network. Copyright © 2020, The Authors. All rights reserved.

关键词： Network coding

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Supervised Online Hashing via Similarity Distribution Learning

arXiv

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arXiv 2019年

作者： Lin, Mingbao Ji, Rongrong Chen, Shen Zheng, Feng Sun, Xiaoshuai Zhang, Baochang Cao, Liujuan Guo, Guodong Huang, Feiyue Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University China Peng Cheng Laboratory China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Automation Science and Electrical Engineering Beihang University China Baidu Research Institute of Deep Learning Co. Ltd China

Online hashing has attracted extensive research attention when facing streaming data. Most online hashing methods, learning binary codes based on pairwise similarities of training instances, fail to capture the semantic relationship, and suffer from a poor generalization in largescale applications due to large variations. In this paper, we propose to model the similarity distributions between the input data and the hashing codes, upon which a novel supervised online hashing method, dubbed as Similarity Distribution based Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship in the produced Hamming space. Specifically, we first transform the discrete similarity matrix into a probability matrix via a Gaussianbased normalization to address the extremely imbalanced distribution issue. And then, we introduce a scaling Student t-distribution to solve the challenging initialization problem, and efficiently bridge the gap between the known and unknown distributions. Lastly, we align the two distributions via minimizing the Kullback-Leibler divergence (KL-diverence) with stochastic gradient descent (SGD), by which an intuitive similarity constraint is imposed to update hashing model on the new streaming data with a powerful generalizing ability to the past data. Extensive experiments on three widely-used benchmarks validate the superiority of the proposed SDOH over the state-of-the-art methods in the online retrieval task. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability distributions

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A generalized model of TiOx-based memristive devices and its application for image processing

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Chinese Physics B 2017年第9期26卷 70-81页

作者：张江伟汤振森许诺王耀孙红辉王之元方粮 State Key Laboratory of High Performance Computing National University of Defense Technology School of Computer National University of Defense Technology Department of Electrical and Computer Engineering University of Pittsburgh Department of Material Science and Engineering College of EngineeringSeoul National University School of Computer National University of Defense Technology

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance（NDR） behavior. This model is physics-oriented and passes Linn＇s criteria. It not only exhibits sufficient accuracy（IV characteristics within 1.5% RMS）, lower latency（below half the VTEAM model）,and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression（LTP/LTD）. Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy（for gray sketching and edge detection, respectively） compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

关键词： memristor modeling memristor-based network gray sketching edge detection

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Segmentation Guided HEp-2 Cell Classification with Adversarial Networks

Segmentation Guided HEp-2 Cell Classification with Adversari...

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computing, Communications and Applications Conference (ComComAp)

作者： Hai Xie Yejun He Haijun Lei Jong Yih Kuo Baiying Lei Guangdong Engineering Research Center of Base Station Antennas and Propagation Shenzhen Key Lab of Antennas and Propagation College of Electronics and Information Engineering Shenzhen University China Guangdong Province Key Laboratory of Popular High-performance Computers School of Computer and Software Engineering Shenzhen University Computer Science and Information Engineering National Taipei University of Technology Taiwan China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University China

ISBN: (数字)9781728119731

ISBN: (纸本)9781728119748

The analysis of indirect immuno fluorescence (IIF) on human epithelial type 2 (HEp-2) cells is of paramount importance for the autoimmune diseases diagnosis. Essentially, accurate segmentation masks can generate rich boundary information, which is beneficial for improving the performance of the classification task For this reason, this paper proposes a novel segmentation guided HEp-2 cell classification method via generative adversarial networks (GANs), which employs the GANs as the segmentor to generate accurate masks for the subsequent classification task. Specifically, the proposed network architecture consists of three modules (i.e., the generator, discriminator and classifier). The first two modules constitute GANs model, which is trained to obtain better segmentation results via playing a min-max game. The segmentation masks and the corresponding original images are fed to the third module together to identify the category of the trained cell. Furthermore, the Xception and ResNet-50 model are used as the backbone of the segmentation and classification network, respectively. Besides, an improved classification loss function via Gaussian Mixture (GM) is proposed to optimize the classification network The proposed architecture can learn rich boundary information and well represent the class label of HEp2 cell images. Experimental results on the HEp-2 International Conference on Pattern Recognition (ICPR) 2016 task1 dataset demonstrate our proposed model achieves quite promising performance.

关键词： Image segmentation computer architecture Task analysis Microprocessors Gallium nitride Feature extraction Generators

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Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer

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Journal of Physics: Conference Series 2021年第1期1755卷

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut N. Ali Angela Amphawan Alaa Fareed Aras Al-dawoodi Awab Noori School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Al-Qalam University College Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University 47500 Petaling Jaya Selangor Malaysia. School of Computing University Utara Malaysia Malaysia. Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed.

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high-rate and high-capacity measurement-deviceindependent quantum key distribution with Fibonacci matrix coding in free space

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science China(information sciences) 2018年第6期61卷 227-237页

作者： Hong LAI Mingxing LUO Josef PIEPRZYK Jun ZHANG Lei PAN Mehmet A.ORGUN School of Computer and Information Science and Centre for Research and Innovation in Software Engineering (RISE) Southwest University Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and Computer Science Queensland University of Technology Institute of Computer Science Polish Academy of Sciences School of Information Technology Deakin University Department of Computing Macquarie University Faculty of Information Technology Macau University of Science and Technology

This paper proposes a high-rate and high-capacity measurement-device-independent quantum key distribution(MDI-QKD) protocol with Fibonacci-valued and Lucas-valued orbital angular momentum(OAM) entangled states in free space. In the existing MDI-OAM-QKD protocols, the main encoding algorithm handles encoded numbers in a bit-by-bit manner. To design a fast encoding algorithm, we introduce a Fibonacci matrix coding algorithm, by which, encoded numbers are separated into segments longer than one bit. By doing so, when compared to the existing MDI-OAM-QKD protocols, the new protocol can effectively increase the key rate and the coding capacity. This is because Fibonacci sequences are used in preparing OAM entangled states, reducing the misattribution errors(which slow down the execution cycle of the entire QKD) in QKD protocols. Moreover, our protocol keeps the data blocks as small as possible, so as to have more blocks in a given time interval. Most importantly, our proposed protocol can distill multiple Fibonacci key matrices from the same block of data, thus reducing the statistical fluctuations in the sample and increasing the final QKD rate. Last but not the least, the sender and the receiver can omit classical information exchange and bit flipping in the secure key distillation stage.

关键词： measurement-device-independent quantum key distribution Fibonacci-matrix coding free space orbital angular momentum bit flipping misattribution errors

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Reports of the AAAI 2019 spring symposium series

Reports of the AAAI 2019 spring symposium series

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作者： Baldini, Ioana Barrett, Clark Chella, Antonio Cinelli, Carlos Gamez, David Gilpin, Leilani H. Hinkelmann, Knut Holmes, Dylan Kido, Takashi Kocaoglu, Murat Lawless, William F. Lomuscio, Alessio Macbeth, Jamie C. Martin, Andreas Mittu, Ranjeev Patterson, Evan Sofge, Donald Tadepalli, Prasad Takadama, Keiki Wilson, Shomir IBM Research AI Department of Computer Science at Stanford University United States Department of Engineering University of Palermo Italy University of California Los Angeles United States Department of Computer Science Middlesex University London United Kingdom Massachusetts Institute of Technology United States FHNW University of Applied Sciences and Arts Northwestern Switzerland Switzerland Massachusetts Institute of Technology United States Visiting Researcher of Stanford University United States Researcher at MIT-IBM Watson AI Lab Paine College United States Department of Computing Imperial College London United Kingdom Smith College United States FHNW University of Applied Sciences and Arts Northwestern Switzerland Switzerland US Naval Research Laboratory Information Management and Decision Architectures Branch within Information Technology Division United States Department of Statistics at Stanford University United States Computer Scientist and Roboticist at US Naval Research Laboratory United States Computer Science at Oregon State University United States University of Electro Communications in Japan Japan College of Information Sciences and Technology at Pennsylvania State University United States

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data;self-driving cars have already been involved in fatalities, including a pedestrian;and yet machine learning is unable to explain the contexts within which it operates. Copyright © 2019, Association for the Advancement of Artificial Intelligence.

关键词： Machine learning

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Task-oriented word embedding for text classification 27

Task-oriented word embedding for text classification

引用

27th International Conference on Computational Linguistics, COLING 2018

作者： Liu, Qian Huang, Heyan Gao, Yang Wei, Xiaochi Tian, Yuxin Liu, Luyang Department of Computer Science Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Centre for Artificial Intelligence University of Technology Sydney Australia

ISBN: (纸本)9781948087506

Distributed word representation plays a pivotal role in various natural language processing tasks. In spite of its success, most existing methods only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. The rational word embeddings should have the ability to capture both the semantic features and task-specific features of words. In this paper, we propose a task-oriented word embedding method and apply it to the text classification task. With the function-aware component, our method regularizes the distribution of words to enable the embedding space to have a clear classification boundary. We evaluate our method using five text classification datasets. The experiment results show that our method significantly outperforms the state-of-the-art methods. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Embeddings

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Connectivity-Aware Virtual Machine Placement in 60 GHz Wireless Cloud Centers 7th

Connectivity-Aware Virtual Machine Placement in 60 GHz Wirel...

引用

7th EAI International Conference on Cloud computing, CloudComp 2016 and 1st EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2016

作者： Kong, Linghe Ye, Linsheng Wang, Bowen Gao, Xiaofeng Wu, Fan Chen, Guihai Hossain, M. Shamim Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

ISBN: (纸本)9783319696041

Benefiting from the 60 GHz technology, physical machines in advanced cloud centers are connected by not only the conventional wired links but also the wireless communications. The 60 GHz millimeter-wave (mmWave) introduces valuable advantages into cloud centers including flexibility, scalability and high rate. Nevertheless, mmWave is constrained by directional communications, i.e., a wireless link is connected if and only if two directional antennas face to each other. This constraint introduces a new problem in cloud service: the virtual machine (VM) placement should consider the real-time connectivity if communications are required between VMs. Otherwise, rotating the antenna costs additional delay, resulting in performance degradation. To address this problem, we propose a novel connectivity-aware VM placement (CAVMP) specialized for 60 GHz wireless cloud center. The core of CAVMP is to dynamically place VMs in order to improve the utilization and avoid overloads while taking the connectivity state into account. We build a 2-rack cloud to measure the connectivity feature of mmWave communications. In addition, we conduct extensive simulations to evaluate CAVMP. performance results demonstrate that CAVMP significantly outperforms existing VM placement schemes in wireless cloud center. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Millimeter waves

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