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SSRN 2022年

作者： Chen, Tong Liu, Guogang Xu, Zhonghui Xiao, Xianbo School of software engineering Jiangxi University of Science and Technology Nanchang330013 China School of Energy and Mechanical Engineering Energy materials computing center Jiangxi University of Science and Technology Nanchang330013 China School of Computer Science Jiangxi University of Chinese Medicine Nanchang330004 China

The successful growth of centimeter-scale monolayer films of MoSi$_{2}$N$_{4}$ and WSi$_{2}$N$_{4}$ has attracted great interest (science 369, 670, 2020). Here, we systematically investigate the effects of biaxial and uniaxial strain on the electronic structures of WSi$_{2}$N$_{4}$ nanosheets with different layers of monolayer(ML), bilayer(BL) and trilayer(TL), respectively, by using first-principles calculations. In addition, combined with the non-equilibrium Green's function methods, the photoelectronic response of WSi$_{2}$N$_{4}$ ML is also explored. The results indicate that: 1) The biaxial and uniaxial strain can flexibly increase or decrease the band gap of WSi$_{2}$N$_{4}$ nanosheets. Particularly, tensile strain realizes the transition of WSi$_{2}$N$_{4}$ nanosheets from semiconductor to metal. 2) The tensile strain effectively improves the optical properties of WSi$_{2}$N$_{4}$ nanosheets in the visible light region, and the red/blue shift of the absorption peaks are observed in the optical spectrum by tensile/compressive strain. 3) The pin-junction photodiode of WSi$_{2}$N$_{4}$ ML generates sizable photocurrents under illumination, showing a strong photoelectronic response to purple light. Furthermore, the biaxial tensile strain significantly enhances the photoelectric performance of the WSi$_{2}$N$_{4}$ photodiode and effectively modulates its detection range in the visible region. Hence, our findings suggest that WSi$_{2}$N$_{4}$ nanosheets are a promising multifunctional material for nanoelectronic and optoelectronic applications. © 2022, The Authors. All rights reserved.

关键词： Monolayers

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Semi-markov CRF Model Based on Stacked Neural Bi-LSTM for Sequence Labeling 3

Semi-markov CRF Model Based on Stacked Neural Bi-LSTM for Se...

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3rd IEEE International Conference of Safe Production and Informatization, IICSPI 2020

作者： Liu, Yupeng Li, Guodong Zhang, Xiaochen School of Software Harbin University of Science and Technology Harbin China School of Computer Science and Technology Harbin University of Science and Technology Harbin China School of Materials Chemical and Engineering Heilongjiang Institute of Technology Harbin China

ISBN: (纸本)9781728177380

In this paper, we present a model for sequence labeling. Based on word vector representations of neural network model algorithms and artificial features of statistical machine learning, a stack Bi-LSTM and new semi-Markov conditional random field frameworks are proposed. On the one hand, we present stack Bi-LSTM to achieve segmentation and chunking task on both word-level and character-level. On the other hand, we use traditional CRF and NSCRF to make some restrictions on the labeling entities to improve the accuracy of labels and then use them to decode the unknown sequence. We test the trained model in the CoNLL2000 and CoNLL2003 datasets. © 2020 IEEE.

关键词： Neural network models

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ALGNN: Auto-Designed Lightweight Graph Neural Network 18th

ALGNN: Auto-Designed Lightweight Graph Neural Network

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Cai, Rongshen Tao, Qian Tang, Yufei Shi, Min School of Software South China University of Technology Guangzhou China Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton United States Institute for Informatics Washington University School of Medicine in St. Louis St. Louis United States

ISBN: (纸本)9783030891879

Graph neural networks (GNNs) are widely used on graph-structured data, and its research has made substantial progress in recent years. However, given the various number of choices and combinations of components such as aggregator and activation function, designing GNNs for specific tasks is very heavy manual work. Recently, neural architecture search (NAS) was proposed with the aim of automating the GNN design process and generating task-dependent architectures. While existing approaches have achieved competitive performance, they are not well suited to practical application scenarios where the computational budget is limited. In this paper, we propose an auto-designed lightweight graph neural network (ALGNN) method to automatically design lightweight, task-dependent GNN architectures. ALGNN uses multi-objective optimization to optimize the architecture constrained by the computation cost and complexity of the model. We define, for the first time, an evaluation standard for consumption cost with the analysis of the message passing process in GNNs. Experiments on real-world datasets demonstrate that ALGNN can generate a lightweight GNN model that has much fewer parameters and GPU hours, meanwhile has comparable performance with state-of-the-art approaches. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition

arXiv

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arXiv 2023年

作者： Liao, Shenghai Liu, Xuya Han, Ruyi Fu, Shujun Zhou, Yuanfeng Li, Yuliang The School of Mathematics Shandong University Jinan250100 China The School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China The School of Software Shandong University Jinan250101 China The Department of Intervention Medicine The Second Hospital of Shandong University Jinan250033 China

Digital image inpainting is an interpolation problem, inferring the content in the missing (unknown) region to agree with the known region data such that the interpolated result fulfills some prior knowledge. Low-rank and nonlocal self-similarity are two important priors for image inpainting. Based on the nonlocal self-similarity assumption, an image is divided into overlapped square target patches (submatrices) and the similar patches of any target patch are reshaped as vectors and stacked into a patch matrix. Such a patch matrix usually enjoys a property of low rank or approximately low rank, and its missing entries are recoveried by low-rank matrix approximation (LRMA) algorithms. Traditionally, n nearest neighbor similar patches are searched within a local window centered at a target patch. However, for an image with missing lines, the generated patch matrix is prone to having entirely-missing rows such that the downstream low-rank model fails to reconstruct it well. To address this problem, we propose a region-wise matching (RwM) algorithm by dividing the neighborhood of a target patch into multiple subregions and then search the most similar one within each subregion. A non-convex weighted low-rank decomposition (NC-WLRD) model for LRMA is also proposed to reconstruct all degraded patch matrices grouped by the proposed RwM algorithm. We solve the proposed NC-WLRD model by the alternating direction method of multipliers (ADMM) and analyze the convergence in detail. Numerous experiments on line inpainting (entire-row/column missing) demonstrate the superiority of our method over other competitive inpainting algorithms. Unlike other low-rank-based matrix completion methods and inpainting algorithms, the proposed model NC-WLRD is also effective for removing random-valued impulse noise and structural noise (stripes). Extension to the restoration of remote sensing images demonstrate that the proposed model has competitive performance for destriping (stripe noise rem

关键词： Approximation algorithms

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Hierarchical Context Modeling Network for Landmark Recognition

Hierarchical Context Modeling Network for Landmark Recogniti...

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IEEE International Conference on Data Mining (ICDM)

作者： Xing Bao Huan Zheng Zhao Zhang Zhengjun Zha Meng Wang School of Information Science and Technology University of Science and Technology of China Hefei China Aerospace Information Research Institute Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China

Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

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RDH-DES: Reversible Data Hiding over Distributed Encrypted-Image Servers Based on Secret Sharing

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ACM Transactions on Multimedia Computing, Communications and Applications 2023年第1期19卷 5-5页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Xia, Zhihua Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Nanjing University of Information Science and Technology No. 219 Ningliu Road Nanjing Jiangsu210044 China Department of Computer Science and Information Engineering National Dong Hwa University No. 1 Sec. 2 Da Hsueh Rd. Hualien974301 Taiwan

Reversible Data Hiding in Encrypted Image (RDHEI) schemes may redistribute the data hiding procedure to other parties and can preserve privacy of the cover image. Recently, cloud computing technology has led to the rapid growth of networked media, and many multimedia rights are owned by multiple parties, such as a film's producer and multiple distributors. Thus, the data hiding task could be distributed to multiple distributed servers. Multi-party data hiding has become an important demand for networked media. In addition, it is essential to preserve multi-server and multi-message privacy and data integrity. However, most of the RDHEI schemes involve only one data hider. That inspired us to design the secure multi-party embedding over distributed encrypted-image servers as a solution for multi-party RDHEI applications. In this article, we propose a novel Reversible Data Hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing. The Chinese remainder theorem, secret sharing, and block-level scrambling are developed as a lightweight cryptography to generate the encrypted image shares. These shares are distributed to different image servers and are used to embed secret data in the proposed framework. The marked encrypted image can be constructed through the marked encrypted shares from different parties, and the decryption and extraction can be completed by the receiver. The experimental results and theoretical analysis have demonstrated that the proposed scheme is secure and effective. © 2023 Association for Computing Machinery.

关键词： Steganography

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Joint Adaptation for Mobile 360-Degree Video Streaming and Enhancement

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IEEE Transactions on Mobile Computing 2025年

作者： Guo, Haotian Wang, Feng Zhang, Wei Zhu, Yifei Cui, Laizhong Liu, Jiangchuan Yu, F. Richard Zhang, Lei Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518060 China The University of Mississippi Department of Computer and Information Science MS38677 United States ByteDance Guangdong Shenzhen518060 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Carleton University School of Information Technology OttawaONK1S 5B6 Canada

Tile-based streaming and super resolution (SR) are two representative technologies adopted to improve bandwidth efficiency of 360° video streaming. The former allows selective downloading of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to enhance the received video to higher quality using advanced neural network models. In this work, we propose a Collaborated Streaming and Enhancement (CSE) adaptation framework for mobile 360° videos, which integrates super resolution with tile-based streaming to optimize the user experience with dynamic bandwidth and limited computing capability. To effectively enhance the tile-based video streaming through SR, we propose to adaptively group the tiles for quality enhancement adapting to the content similarity. We also identify and address several key design issues to integrate SR into tile-based video streaming including unified video quality assessment, computational complexity model for super resolution, and buffer analysis considering the interplay between transmission and enhancement. We further formulate the quality-of-experience (QoE) maximization problem for mobile 360° video streaming and propose a rate adaptation algorithm to make the best decisions for download and for enhancement based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which demonstrates stable performance with considerable QoE improvement, while enabling a trade-off between playback smoothness and video quality. © 2002-2012 IEEE.

关键词： Video streaming

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Image Inpainting Exploiting Global Prior Refined Weighted Low-Rank Representation

SSRN

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SSRN 2023年

作者： Liao, Shenghai Liu, Xuya Han, Ruyi Fu, Shujun Zhou, Yuanfeng Li, Yuliang School of Mathematics Shandong University Jinan250100 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China School of Software Shandong University Jinan250101 China Department of Intervention Medicine The Second Hospital of Shandong University Jinan250033 China

Low-rank and nonlocal self-similarity are important priors for image restoration. Based on nonlocal self-similarity assumption, similar patches are grouped into a matrix that is of low-rank or approximately low-rank. Traditional algorithms model low-rank prior of local group matrices (intra-group) and ignore the global inter-group relationship, which will result in suboptimal performance without considering global consistency. Due to overlapping of patches, groups are dependent, however, conventional methods process each group independently. To make full use of the intrinsic relationship among groups, we consider all groups simultaneously and propose a global prior refined weighted low-rank representation model for image inpainting. The local-groups and the aggregation of all groups are taken into account in the proposed model. By introducing auxiliary variable, we decouple the local-global model and optimize iteratively using the alternating direction method of multipliers. The proposed global refinement model significantly improves the performance of local low-rank model. A new strategy for grouping similar patches is also proposed by dividing the neighborhood of a target patch into multiple subregions and searching for a given number of similar patches within each subregion. The proposed method is applied to various tasks (line inpainting, destriping, and depth map completion) and obtains promising performance. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Image Inpainting Exploiting Global Prior Refined Weighted Low-Rank Representation

SSRN

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SSRN 2023年

关键词： Image reconstruction

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Scatter Balance based Semi-supervised Dimensional Reduction 4

Scatter Balance based Semi-supervised Dimensional Reduction

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4th IEEE International Conference on Smart Internet of Things, SmartIoT 2020

作者： Yang, Rui Meng, Xiangzhu Feng, Lin Dalian University of Technology School of Computer Science and Technology Dalian China Software College of Shenyang Normal University Shenyang China School of Computer Science and Technology of Dalian University of Technology School of Innovation and Entrepreneurship of Dalian University of Technology Dalian China

ISBN: (纸本)9781728165141

Dimensionality reduction is an important research direction for machine learning. In recent years, semi-supervised learning has become a hot issue. Especially, SDA and SMMC are two representative semi-supervised dimensionality reduction methods, which mainly focus on the combination of local geometry structure and label information. For this reason, SDA and SMMC are deeply analyzed in this paper. However, the subspaces generated by these two methods only focus on within-class scatter or between-class scatter. Here we propose a new semi-supervised dimensionality reduction method that can balance them through the co-Angle method. The experiment on real-world datasets can illustrate the efficiency of our method. © 2020 IEEE.

关键词： Learning systems

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