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International Conference on Systems and Informatics (ICSAI)

作者： Yi Chen Bin Sun Meiling Feng Junqing Sun Tianjin Key Laboratory of Intelligent Computing & Novel Software Tianjin University of Technology Tianjin China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

In recent years, “Internet +” and e-commerce technology have had a profound impact on the recycling industry. Consumer psychology, as the most important factor in the positive sales chain, has received little attention in the recycling field. Based on the consumer's acceptance of online recycling channels and sensitivity to recycling prices, this paper investigates a two-channel reverse supply chain system consisting of the brand owner and a dismantling center. Firstly, the pricing strategy model, which takes consumer psychology into account, is established based on the Stackelberg game. It is found that the consumer's acceptance of the online recycling channel will reduce the profit of the dismantling center, but has no effect on the profit of the brand owner. Consumers' sensitivity to price will increase the profits of both the brand owner and the dismantling center, and will also make the optimal pricing of both parties tend to be fixed. Finally, the simulation experiment is carried out to verify the influence of psychological indicators on the pricing strategy and profit of the brand owner and dismantling center, and the optimal pricing strategy of both models is obtained after the online acceptance index and price sensitivity index are determined.

关键词： Recycling Pricing Psychology Consumer electronics Games Analytical models Supply chains

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Universal deep network for steganalysis of color image based on channel representation

arXiv

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

引用

Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xin Chen Yuanjie Zheng Yi Niu Chengiiang Li School of Information Science and Engineering Shandong Normal University Jinan China Key Lab of Intelligent Computing and Information Security Universities of Shandong Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan China Shandong University of Science and Technology Jinan China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194820

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.

关键词： Epilepsy Electroencephalography Convolution Brain modeling Feature extraction Correlation Task analysis

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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On-line Detection and Analysis of Alloy Steel Elements Based on the LIBS technology and Random Forest Regression 37

On-line Detection and Analysis of Alloy Steel Elements Based...

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37th Chinese Control Conference, CCC 2018

作者： Jixuan, Yang Qin, Zhang Peng, Yang Junqing, Sun Tianjin University of Technology Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9789881563941

The Laser Induced Breakdown Spectroscopy (LIBS) technology can be used to detect the elements in the alloy steel in real time. Quantitative analysis method of the traditional LIBS technology mainly has the calibration method and calibration free method, but there are two shortcomings: low prediction accuracy and over fitting. Random Forest Regression (RFR) algorithm can be used for classification and regression, can effectively avoid 'overfitting' phenomenon. Therefore, in this paper, we combine the random forest regression algorithm with laser induced breakdown spectroscopy applied to the detection of the concentration of alloy steel elements in the metallurgy industry. At the same time, compared with partial least squares method based on the LIBS, the results show that the random forest algorithm combined with the LIBS technology has the higher prediction accuracy, lower root mean square error and better robustness. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Laser induced breakdown spectroscopy

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Joint Configuration Adaptation and Bandwidth Allocation for Edge-based Real-time Video Analytics

Joint Configuration Adaptation and Bandwidth Allocation for ...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Can Wang Sheng Zhang Yu Chen Zhuzhong Qian Jie Wu Mingjun Xiao State Key Lab. for Novel Software Technology Nanjing University P.R. China Center for Networked Computing Temple University School of Computer Science and Technology / Suzhou Institute for Advanced Study University of Science and Technology of China P.R. China

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to the nearby server, thus mitigating long latency incurred by data transmission via wide area networks. When offloading frames from the front-end device to the edge server, the application configuration (frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration adaption and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. Simulation results show that JCAB can effectively balance the analytics accuracy and energy consumption while keeping low system latency.

关键词： Streaming media Bandwidth Energy consumption Servers Channel allocation Image resolution Energy resolution

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Multi-user Edge-assisted Video Analytics Task Offloading Game based on Deep Reinforcement Learning

Multi-user Edge-assisted Video Analytics Task Offloading Gam...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu Chen Sheng Zhang Mingjun Xiao Zhuzhong Qian Jie Wu Sanglu Lu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Computer Science and Technology / Suzhou Institute for Advanced Study University of Science and Technology of China P.R. China Center for Networked Computing Temple University

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

With the development of deep learning, artificial intelligence applications and services have boomed in the recent years, including recommendation systems, personal assistant and video analytics. Similar to other services in the edge computing environment, artificial intelligence computing tasks are pushed to the network edge. In this paper, we consider the multi-user edge-assisted video analytics task offloading (MEVAO) problem, where users have video analytics tasks with various accuracy requirements. All users independently choose their accuracy decisions, satisfying the accuracy requirement, and offload the video data to the edge server. With the utility function designed based on the features of video analytics, we model MEVAO as a game theory problem and achieve the Nash equilibrium. For the flexibility of making accuracy decisions under different circumstances, a deep reinforcement learning approach is applied to our problem. Our proposed design has much better performance compared with some other approaches in the extensive simulations.

关键词： Visual analytics Reinforcement learning Games Nash equilibrium Servers Task analysis Artificial intelligence

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A Kind of new parallax algorithm based on symmetric continuous optimization

引用

Journal of Physics: Conference Series 2021年第3期1748卷

作者： Kaiwen Jiang Degan Zhang Haixia Xu Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education 300384 China Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin 300410 China

With the fast development of 3D imaginations becomes more and more fascination, multi-view stereo based 3D reconstruction is a significant technique for those application. To facilitate the subsequent processing of 3D reconstruction and reduce the possibility of other algorithms falling into local optimal solutions, attempting to get better and faster performance, a new parallax calculation based on symmetric continuous optimization is proposed in this paper. The algorithm is proposed here will be tested respectively in the same data, in order to certificate the algorithm applied is better than traditional minimize El algorithm.

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Brief review of image denoising techniques

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Visual computing for Industry,Biomedicine,and Art 2019年第1期2卷 55-66页

作者： Linwei Fan Fan Zhang Hui Fan Caiming Zhang School of Software Shandong UniversityShunHua Road No.1500Jinan 250101China Shandong Co-Innovation Center of Future Intelligent Computing BinHai Road No.191Yantai 264005China Shandong Province Key Lab of Digital Media Technology Shandong University of Finance and EconomicsBicyclic East Road No.7366Jinan 250061China

With the explosion in the number of digital images taken every day,the demand for more accurate and visually pleasing images is ***,the images captured by modern cameras are inevitably degraded by noise,which leads to deteriorated visual image ***,work is required to reduce noise without losing image features(edges,corners,and other sharp structures).So far,researchers have already proposed various methods for decreasing *** method has its own advantages and *** this paper,we summarize some important research in the field of image ***,we give the formulation of the image denoising problem,and then we present several image denoising *** addition,we discuss the characteristics of these ***,we provide several promising directions for future research.

关键词： Image denoising Non-local means Sparse representation Low-rank Convolutional neural network

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Multi-objective generalized linear bandits

arXiv

引用

arXiv 2019年

作者： Lu, Shiyin Wang, Guanghui Hu, Yao Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China YouKu Cognitive and Intelligent Lab Alibaba Group Beijing100102 China

In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applications typically contain contextual information that can guide the learning process which, however, is ignored by most of existing work. To utilize this information, we associate each arm with a context vector and assume the reward follows the generalized linear model (GLM). We adopt the notion of Pareto regret to evaluate the learner's performance and develop a novel algorithm for minimizing it. The essential idea is to apply a variant of the online Newton step to estimate model parameters, based on which we utilize the upper confidence bound (UCB) policy to construct an approximation of the Pareto front, and then uniformly at random choose one arm from the approximate Pareto front. Theoretical analysis shows that the proposed algorithm achieves an Õ(d√T) Pareto regret, where T is the time horizon and d is the dimension of contexts, which matches the optimal result for single objective contextual bandits problem. Numerical experiments demonstrate the effectiveness of our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Pareto principle

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