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1st International Conference on Computational Visual Media, CVM 2012

作者： Qin, Xueying Zhang, Rui Lin, Lili Zhong, Fan Xing, Guanyu Peng, Qunsheng School of Computer Science and Technology Shandong University China Shandong Provincial Key Laboratory of Network Based Intelligent Computing China State Key Laboratory of CAD and CG Zhejiang University China

ISBN: (纸本)9783642342622

In augmented reality, it is required to sense the changing of the light condition for achieving illumination consistency. In this paper, we build up the decomposition equation of basis images of static scenes for this purpose. It is proved that the basis images are invariants of the scene, which is the global illumination effects of a distributed light source with unit power, and it is unnecessary to assume that the reflectance of appearance of objects in scenes is ideal diffuse. Our method can also be applied in image understanding and compressing. © 2012 Springer-Verlag.

关键词： Augmented reality

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Reliable neighbors-based collaborative filtering for recommendation systems 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Sun, Xiaohan Cheng, Jiangfei Li, Zepeng School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Collaborative filtering

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An image encryption algorithm based on compressed sensing and dual-tree complex wavelet transform 17

An image encryption algorithm based on compressed sensing an...

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5th International Conference on Bioinformatics and Computational Biology, ICBCB 2017

作者： Dong, Jiwen Zhao, Ziru Li, Hengjian Department of Computer Science and Technology Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan City Shandong Province China

ISBN: (纸本)9781450348270

Combined image encryption with the process of compressed sensing (CS) which based on dual-tree complex wavelet transform, we came up with the image encryption algorithm which based on the theory of CS and double random phase encoding (DRPE). Firstly, adopting DT-CWT represent the image sparsely. It can enhance the image edge and texture, it also can attain directional feature information. Secondly, utilizing chaotic system to product chaos sequence as the measurement matrix on the process of CS. At the same time, the values of initial chaotic parameters are the keys. Finally, we selected DRPE on measurements to finish the process of secondary encryption. The values of seeds which are used to generate two random phase masks are the keys of secondary encryption. In the process of decryption, providing the correct values of keys can generate the chaotic measurement matrix and random phase masks to be used for decryption. Orthogonal matching pursuit (OMP) algorithm is employed to reconstruct the original image approximately. Compared with several existing algorithms, this encryption scheme can reconstruct a higher quality image. It has higher robustness and higher sensitivity to the initial keys. © 2017 ACM.

关键词： Cryptography

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A new digital video watermark algorithm based on the HVS

A new digital video watermark algorithm based on the HVS

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2011 International Conference on Internet Computing and Information Services, ICICIS 2011

作者： Liao, Lufang Zheng, Xiaoshi Zhao, Yanling Liu, Guangqi Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Network Technology Jinan China School of Information Science and Technology Shandong Institute of Light Industry SDILI Jinan China

ISBN: (纸本)9780769545394

The algorithm applied to video watermark based on HVS is rare recently , but adaptively selecting the embedding position and the embedding strength according to the host(such as videos, images etc.) is necessary, which can make robustness and invisibility best balanced. This article gives a new algorithm about digital video watermarking based on HVS, which makes digital video watermark greatly improved. © 2011 IEEE.

关键词： Multimedia systems

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Semi-supervised feature selection using sparse laplacian support vector machine 1

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Zheng, Xiaohan Xu, Zhiqiang School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

Semi-supervised feature selection is an active topic in machine learning and data mining. Laplacian support vector machine (LapSVM) has been successfully applied to semi-supervised learning. However, LapSVM cannot be directly applied to feature selection. To remedy it, we propose a sparse Laplacian support vector machine (SLapSVM) and apply it to semi-supervised feature selection. On the basis of LapSVM, SLapSVM introduces the 1-norm regularization, which means the solution of SLapSVM has sparsity. In addition, the training procedure of SLapSVM can be formulated as solving a quadratic programming problem, which indicates that the solution of SLapSVM is unique and global. SLapSVM can perform feature selection and classification at the same time. Experimental results on semi-supervised classification problems show the feasibility and effectiveness of the proposed semi-supervised learning algorithms. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Support vector machines

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Palm-print recognition algorithm based on random projection and chaotic system 17

Palm-print recognition algorithm based on random projection ...

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5th International Conference on Bioinformatics and Computational Biology, ICBCB 2017

作者： Dong, Jiwen Zhao, Ziru Li, Hengjian Feng, Guang Department of Computer Science and Technology Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan City Shandong Province China

ISBN: (纸本)9781450348270

To improve the efficiency and security of palm-print recognition, we proposed a palm-print algorithm which based on random projection and chaotic system. Using twice random projections based on chaotic system to process the palm-print image, it generated the random project matrix and palm-print which are equivalent to two-factor identity authentications. And the initial value of chaotic system also could be the key to protect the security of palm-print. Reducing the dimensionality of the transformation matrix which could be produced randomly and the transformation matrix was not depend on the original training sample completely. In addition, the process of producing the random project matrix is simple and has lower computational complexity. And users could change the key by regain the new random project matrix to protect their privacy. © 2017 ACM.

关键词： Palmprint recognition

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Mobility-Aware Multi-Task Migration and Offloading Scheme for Internet of Vehicles

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Chinese Journal of Electronics 2023年第6期32卷 1192-1202页

作者： LI Xujie TANG Jing XU Yuan SUN Ying College of Computer and Information Hohai University State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Key Lab for Wireless Sensor Network and Communication Shanghai Institute of Microsystem School of Information Engineering Jiangsu Open University

In Internet of vehicles, vehicular edge computing(VEC) as a new paradigm can effectively accomplish various tasks. Due to limited computing resources of the roadside units(RSUs), computing ability of vehicles can be a powerful supplement to computing resources. Then the task to be processed in data center can be offloaded to the vehicles by the RSUs. Due to mobility of the vehicles, the tasks will be migrated among the RSUs. How to effectively offload multiple tasks to the vehicles for processing is a challenging problem. A mobility-aware multi-task migration and offloading scheme for Internet of vehicles is presented and analyzed. Considering the coupling between migration and offloading, the joint migration and offloading optimization problem is formulated. The problem is a NP-hard problem and it is very hard to be solved by the conventional methods. To tackle the difficult problem, the idea of alternating optimization and divide and conquer is introduced. The problem can be decoupled into two sub-problems: computing resource allocation problem and vehicle node selection problem. If the vehicle node selection is given, the problem can be solved based on Lagrange function. And if the allocation of computing resource is given, the problem turns into a 0-1 integer programming problem, and the linear relaxation of branch bound algorithm is introduced to solve it. Then the optimization value is obtained through continuous iteration. Simulation results show that the proposed algorithm can effectively improve system performance.

关键词： Costs Computational modeling System performance Simulation Programming Multitasking Resource management

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QoE-Driven Big Data Management in Pervasive Edge Computing Environment

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Big Data Mining and Analytics 2018年第3期1卷 222-233页

作者： Qianyu Meng Kun Wang Xiaoming He Minyi Guo Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai Jiao Tong University

In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience(QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two ***, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural network(TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.

关键词： Quality-of-Experience(QoE) high-dimensional big data management deep learning pervasive edge computing

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Three-mobile-beacon assisted weighted centroid localization method in wireless sensor networks

Three-mobile-beacon assisted weighted centroid localization ...

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2011 IEEE 2nd International Conference on Software Engineering and Service Science, ICSESS 2011

作者： Cui, Huan-Qing Wang, Ying-Long Lv, Jia-Liang Mao, Yu-Ming College of Information Science and Engineering Shandong University of Science and Technology Qingdao China Shandong Provincial Key Laboratory of Computer Network Shandong Computer Science Center Jinan China

ISBN: (纸本)9781424496969

Localization is one of the key technologies in wireless sensor networks, and the mobile beacon assisted localization method is promising to reduce the cost. The methods with only one beacon introduce collinearity problem which degrades performance. This paper proposes a weighted centroid localization method using three mobile beacons. These beacons preserve a special formation while traversing the network deployment area, and broadcast their positions periodically. The sensor nodes to be localized estimate the distances to these beacons, and utilize weighted centroid localization scheme to calculate its position. Simulation shows that this method is superior to trilateration and weighted centroid algorithm with single mobile beacon. © 2011 IEEE.

关键词： Sensor nodes

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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