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Provably Secure APK Redevelopment Authorization Scheme in the Standard Model

computers, Materials & Continua 2018年第9期56卷 447-465页

作者： Daofeng Li Mingxing Luo Bowen Zhao Xiangdong Che School of Computer Electrical and informationGuang Xi UniversityNanning530004China Information Security and National Computing Grid Laboratory Southwest Jiaotong UniversityChengdu610031China Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi UniversityNanning530004China School of Information Security&Applied Computing College of TechnologyEastern Michigan UniversityMichigan48197USA

The secure issues of APK are very important in Android *** order to solve potential secure problems and copyrights issues in redevelopment of APK files,in this paper we propose a new APK redevelopment mechanism(APK-SAN).By exploring sanitizable signature technology,APK-SAN allows the original developer to authorize specified modifier who can redevelop the designated source code of APK *** scheme does not require interactions between the developer and *** can reduce the communication overhead and computational overhead for ***,the signature of redeveloped APK files is valid and maintains the *** proposed APK-SAN signature can effectively protect the security of the redeveloped APK files and copyrights of the developer and modifier.

关键词： Sanitizable signature APK signature mechanism redevelopment ID-based signature scheme

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ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods 12

ECNU at SemEval-2018 Task 3: Exploration on Irony Detection ...

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12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the

作者： Yin, Zhenghang Wang, Feixiang Lan, Man Wang, Wenting Department of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing China Alibaba Group

ISBN: (纸本)9781948087209

The paper describes our submissions to task 3 in SemEval 2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes. To address them, we explored supervised machine learning method alone and in combination with neural networks. © 2018 Association for Computational Linguistics

关键词： Supervised learning

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One-dimensional deep low-rank and sparse network for accelerated MRI

arXiv

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arXiv 2021年

作者： Wang, Zi Qian, Chen Guo, Di Sun, Hongwei Li, Rushuai Zhao, Bo Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China School of Computer and Information Engineering Xiamen University of Technology Xiamen China United Imaging Research Institute of Intelligent Imaging Beijing China Department of Nuclear Medicine Nanjing First Hospital Nanjing Medical University Nanjing China Department of Biomedical Engineering Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin United States

Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed ODLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, ODLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1D deep learning scheme is memory-efficient and robust in fast MRI. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Class specific or shared? A hybrid dictionary learning network for image classification

arXiv

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arXiv 2019年

作者： Shao, Shuai Wang, Yan-Jiang Liu, Bao-Di Xu, Rui Li, Ye College of Information and Control Engineering China University of Petroleum Qingdao266580 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology Jinan250000 China

Dictionary learning methods can be split into two categories: i) class specific dictionary learning ii) class shared dictionary learning. The difference between the two categories is how to use the discriminative information. With the first category, samples of different classes are mapped to different subspaces which leads to some redundancy in the base vectors. For the second category, the samples in each specific class can not be described well. Moreover, most class shared dictionary learning methods use the 0-norm regularization term as the sparse constraint. In this paper, we first propose a novel class shared dictionary learning method named label embedded dictionary learning (LEDL) by introducing the 1-norm sparse constraint to replace the conventional 0-norm regularization term in LC-KSVD method. Then we propose a novel network named hybrid dictionary learning network (HDLN) to combine the class specific dictionary learning with class shared dictionary learning together to fully describe the feature to boost the performance of classification. Extensive experimental results on six benchmark datasets illustrate that our methods are capable of achieving superior performance compared to several conventional classification algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Research and Prospect of Digital Economy Development in Shandong Province

Research and Prospect of Digital Economy Development in Shan...

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2019 5th International Conference on Humanities and Social Science Research (ICHSSR 2019)

作者： Zhengqian Feng Hao Wang Liping Wei Shandong Information Resources Application Association Qilu University of Technology(Shandong Academy of Sciences) Shandong Computer Science Center(National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong University of Science and Technology

Digital economy refers to the economic model that takes digital technology as the core to drive the whole economic activity process and create *** the future, all economic links may be driven by digital technology, digital technology will be the main driving force for world economic development, and digital economy will be the new engine for world economic *** great transformation of digital economy is a new opportunity for economic *** development level of digital economy in Shandong province has entered the first echelon in China, but there are still some factors restricting its *** digital economy has become the core force leading the scientific and technological revolution and industrial transformation, and human society is entering a new stage marked by digital *** province must correctly grasp the general trend of the development of digital economy, do a good job in planning and creating momentum and taking advantage of it, accelerate the construction of a strong digital province, make the digital economy become an important starting point to promote the conversion of old and new drives and an important supporting force of "lead the way".

关键词： Digital Economy Development Status Evaluation Index

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Surface Reconstruction from Normals: A Robust DGP-based Discontinuity Preservation Approach

Surface Reconstruction from Normals: A Robust DGP-based Disc...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Wuyuan Xie Miaohui Wang Mingqiang Wei Jianmin Jiang Jing Qin College of Computer and Software Engineering Shenzhen University (SZU) Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Nursing Hong Kong Polytechnic University

ISBN: (纸本)9781728132945

In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.

关键词： Surface reconstruction discontinuities angular misalignment Solution space CONSERVATION optimum performance Arts complexity classes

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ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation

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computers in biology and medicine 2025年 194卷 110527页

作者： Xuesheng Bian Jia Liu Sen Xu Weiquan Liu Leyi Mei Chaoshen Xiao Fan Yang School Of Information Engineering Yancheng Institute Of Technology Hope Avenue Middle Road Yancheng 224051 Jiangsu China. Electronic address: xsbian@***. School Of Information Engineering Yancheng Institute Of Technology Hope Avenue Middle Road Yancheng 224051 Jiangsu China. College of Computer Engineering Jimei University Yinjiang Road No. 183 Xiamen 361005 Fujian China. National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility) Key Laboratory of Genetic Evolution & Animal Models and National Resource Center for Nonhuman Primates Kunming Institute of Zoology Chinese Academy of Sciences East Jiaochang Road No. 32 Kunming 650221 Yunnan China. National Key Laboratory of Radar Signal Processing Xidian university No. 2 South Taibai Road Xian 710071 Shaanxi China. School of Resources and Environmental Engineering Wuhan University of Science and Technology 947 Heping Avenue Qingshan District Xian 430081 Wuhan China.

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies, they often perform poorly in handling issues such as similar intensity distributions, irregular lesion shapes, and blurry boundaries, leading to low segmentation accuracy. To address these issues, we propose the ThreeF-Net, a fine-grained feature fusion network. This network combines the advantages of CNNs and Transformers, aiming to simultaneously capture local features and model long-range dependencies, thereby improving the accuracy and stability of segmentation tasks. Specifically, we designed a Transformer-assisted Dual Encoder Architecture (TDE), which integrates convolutional modules and self-attention modules to achieve collaborative learning of local and global features. Additionally, we designed a Global Group Feature Extraction (GGFE) module, which effectively fuses the features learned by CNNs and Transformers, enhancing feature representation ability. To further improve model performance, we also introduced a Dynamic Fine-grained Convolution (DFC) module, which significantly improves lesion boundary segmentation accuracy by dynamically adjusting convolution kernels and capturing multi-scale features. Comparative experiments with state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that ThreeF-Net outperforms existing methods across multiple key evaluation metrics.

关键词： Attention mechanism Breast lesion segmentation Deep learning Feature fusion Ultrasound images

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Pressure-induced enhancement of optoelectronic properties in PtS2

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Chinese Physics B 2018年第6期27卷 345-349页

作者： Yi-Fang Yuan Zhi-Tao Zhang Wei-Ke Wang Yong-Hui Zhou Xu-Liang Chen Chao An Ran-Ran Zhang Ying Zhou Chuan-Chuan Gu Liang Li Xin-Jian Li Zhao-Rong Yang Department of Physics and Laboratory of Material Physics Zhengzhou UniversityZhengzhou 450052China Anhui Provincial Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field LaboratoryChinese Academy of SciencesHefei 230031China Synergetic Innovation Center for Quantum Effects and Application Key Laboratory of Low-dimensional Quantum Structures and Quantum Control of Ministry of EducationCollege of Physics and Information ScienceHunan Normal UniversityChangsha 410081China State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and EngineeringHuazhong University of Science and Technology(HUST)Wuhan 430074China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China

PtS2, which is one of the group-10 transition metal dichalcogenides, attracts increasing attention due to its extraordinary properties under external modulations as predicted by theory, such as tunable bandgap and indirect-to-direct gap transition under strain; however, these properties have not been verified experimentally. Here we report the first experimental exploration of its optoelectronic properties under external pressure. We find that the photocurrent is weakly pressuredependent below 3 GPa but increases significantly in the pressure range of 3 GPa–4 GPa, with a maximum ～ 6 times higher than that at ambient pressure. X-ray diffraction data shows that no structural phase transition can be observed up to26.8 GPa, which indicates a stable lattice structure of PtS2 under high pressure. This is further supported by our Raman measurements with an observation of linear blue-shifts of the two Raman-active modes to 6.4 GPa. The pressure-enhanced photocurrent is related to the indirect-to-direct/quasi-direct bandgap transition under pressure, resembling the gap behavior under compression strain as predicted theoretically.

关键词： high pressure optoelectronic properties transition metal disulfide

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One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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