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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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Adaptive Algorithm Based on Reversible Data Hiding Method for JPEG Images 7th

Adaptive Algorithm Based on Reversible Data Hiding Method fo...

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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： Zhang, Hao Yin, Zhaoxia Zhang, Xinpeng Chen, Jianpeng Wang, Ruonan Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (纸本)9783319696041

This paper presents an adaptive reversible information hiding algorithm that can maintain thee JPEG file sizes by using RLC (Run Length Coding) AC coefficient coded for embedding, the key point is to choose the appropriate number of participation to hide information. By calculating the maximum storage capacity of the image at different system, select the appropriate RLC pairs to rotate and embed data. In the extraction stage, by calculating the sequence of the original RLC pairs status, then consult the mapping relationships between the current sequence and the original RLC pairs sequence, we extract the secret message and recover the original image. Test results proved that the proposed method can improve the rate-distortion performance to some extent. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Adaptive algorithms

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An improved lossless data hiding scheme in JPEG bitstream by VLC mapping 1

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13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017

作者： Du, Yang Yin, Zhaoxia Zhang, Xinpeng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (数字)9789811088902

ISBN: (纸本)9789811088896

This paper proposed a lossless data hiding scheme by variable length code (VLC) mapping, which focused on embedding additional data into JPEG bitstream. The entropy-coded data in JPEG bitstream consists of a sequence of VLCs and the appended bits. Not all VLCs defined in the JPEG file header are used in the entropy-coded data and the replacement of unused-VLCs do nothing with image decompression. Hence, additional data can be embedded by mapping the unused-VLCs to the used-VLCs. To obtain higher embedding capacity, we improved the mapping rules in this paper. Employing the proposed mapping scheme, larger embedding capacity and no image distortion are both achieved while the filesize of JPEG is preserved after data embedding. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Mapping

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HMOE-Net: Hybrid Multi-scale Object Equalization Network for Intracerebral Hemorrhage Segmentation in CT Images

HMOE-Net: Hybrid Multi-scale Object Equalization Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xizhi He Kai Chen Kai Hu Zhineng Chen Xuanya Li Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Affiliated Hospital of Xiangnan University Chenzhou China Institute of Automation Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China College of Medical Imaging and Inspection Xiangnan University Chenzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

In this paper, we propose a novel Hybrid Multi-scale Object Equalization Network (HMOE-Net) to segment intracerebral hemorrhage (ICH) regions. In particular, we design a shallow feature extraction network (SFENet) and a deep feature extraction network (DFENet) to solve the problem of equalization learning of hybrid multi-scale object features. The multi-level feature extraction (MLFE) blocks are presented in DFENet to explore multi-level semantic features more effectively. Furthermore, we adopt a progressive feature extraction strategy combining SFENet and DFENet to further consider the differences of various ICH regions and achieve the equalization feature learning of multi-scale objects. To verify the effectiveness of HMOE-Net, we collect a clinical ICH dataset with a total of 500 CT cases from three hospitals for the evaluation. The experimental results show that HMOE-Net is superior to six state-of-the-art methods and achieves accurate segmentation for multi-scale ICH regions.

关键词： Feature extraction Semantics Image segmentation Data mining Hemorrhaging Computed tomography Convolution

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A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

arXiv

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arXiv 2019年

作者： Zhang, Dong Zhao, Shu Duan, Zhen Chen, Jie Zhang, Yanping Tang, Jie Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So, the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this paper, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC. Firstly, we introduce a HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a Multi-Label Classification (MLC) issue, whose multiple research labels exactly guide the learning process. It's flexible that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It's interesting that we also explore the paper-reviewer recom

关键词： Digital libraries

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Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

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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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Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

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Journal of Beijing Institute of Technology 2018年第4期27卷 592-603页

作者： Gensheng Hu Min Li Dong Liang Mingzhu Wan Wenxia Bao Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui University Hefei 230039China School of Electronics and Information Engineering Anhui UniversityHefei 230601 China School of Information Science and Technology Fudan UniversityShanghai 200433China Anhui Key Laboratory of Polarization Imaging Detection Technology Hefei 230031China

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric ***-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and *** improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient *** extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization *** group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric *** results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

关键词： video surveillance group activity recognition non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT) Cayley-Klein metric learning

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An Improved ACO with First-job Restriction and P-ANT for Batch Scheduling Problem

An Improved ACO with First-job Restriction and P-ANT for Bat...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Yang Yang Zhaohong Jia Yiwen Zhang Kai Li Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

This research applies ant colony optimization (ACO) algorithm to minimizing the makespan and the maximum tardiness for scheduling the jobs with non-identical sizes and release time on parallel batch process machines. To improve search efficiency, a new first job selection strategy and a dynamic heuristic are provided. Additionally, a new role, called P-ANT, is designed to improve the search ability of the ant colony. To solve the studied problem, a novel multi-objective ACO algorithm with P-ANT (MACOP) is proposed. The MACOP is compared with several other state-of-the-art algorithms through extensive experiments. The results demonstrate the effectiveness of the proposed MACOP algorithm.

关键词： Heuristic algorithms Job shop scheduling Genetic algorithms signal processing algorithms Optimization Batch production systems Ant colony optimization

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Reversible data hiding based on reducing invalid shifting of pixels in histogram shifting

arXiv

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arXiv 2019年

作者： Jia, Yujie Yin, Zhaoxia Zhang, Xinpeng Luo, Yonglong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China School of Computer Science Fudan University Shanghai201203 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu Anhui240002 China

In recent years, reversible data hiding (RDH), a new research hotspot in the field of information security, has been paid more and more attention by researchers. Most of the existing RDH schemes do not fully take it into account that natural image's texture has influence on embedding distortion. The image distortion caused by embedding data in the image's smooth region is much smaller than that in the unsmooth region, essentially, it is because embedding additional data in the smooth region corresponds to fewer invalid shifting pixels (ISPs) in histogram shifting. Thus, we propose a RDH scheme based on the images texture to reduce invalid shifting of pixels in histogram shifting. Specifically, first, a cover image is divided into two sub-images by the checkerboard pattern, and then each sub-image's fluctuation values are calculated. Finally, additional data can be embedded into the region of sub-images with smaller fluctuation value preferentially. The experimental results demonstrate that the proposed method has higher capacity and better stego-image quality than some existing RDH schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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Modeling of graphene based strong coulping nanodevice

Modeling of graphene based strong coulping nanodevice

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Xingang Ren Yong Wang Hao Ren Zhixiang Huang Xianliang Wu Guangdong Provincial Key Laboratory of Nano-Micro Materials Research Peking University Shenzhen Graduate School Shenzhen China Ministry of Education Institute of Physical Science and Information Technology Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (数字)9780996007894

ISBN: (纸本)9781728146331

The monolayer graphene has received intensive attention due to its unique characteristics, such as atomic thickness, tunable Fermi level by doping. Here, we will present comprehensive studies from theoretical modeling to application of graphene based nanodevice. Our work indicates that graphene can be considered as bulk material with an effective thickness. In addition, we propose graphene as nanospacer forming a strong coupling sandwiched nanodevice, which realizes strong coupling between localized surface plasmon of metal nanoparticle and surface plasmon of planar metal film. Our work is of critical importance for graphene modeling and applications.

关键词： Graphene Plasmons Conductivity Metals Couplings Permittivity Sensors

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