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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Yunqi Li Jing Liu Bin Jiang Changlin Yang Qingtian Wang School of Computer Science Zhongyuan University of Technology State Information Center Beijing China China Scholarship Council School of Software Engineering Sun Yat-sen University China Telecom Research Institute Beijing China

With an increasing number of Mobile Users (MUs), Multi-access edge computing (MEC) has become a bottleneck in resource limitation. Serverless edge computing (SEC) is a promising approach to effectively alleviate the shortage of MEC. However, existing research on SEC focus on the operating mode of the SEC server, they ignore the interaction between SEC and MU. To this end, we propose a Stackelberg game approach to maximize the utility of each MU. We present the model of the Stackelberg game and propose an iterative algorithm as the solution. We also consider the impact of the function resource pool and using renewable energy on SEC. In particular, when a function that required by a MU is not stored in this SEC, it downloads the function from could with extra cost. Meanwhile, the SEC has a lower cost by using harvested energy rather than purchasing from the grid. Simulation results show that the proposed scheme is efficient in terms of SEC’s profit and MU’s demand. Moreover, both MUs and SECs gain benefits from renewable energy.

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Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

Multi-MedVit: a deep learning approach for the diagnosis of ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunjie Cai Zeqi Zheng Shanling Nie Yue Guo Ruijie Zhang Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai PR China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study’s 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly.

关键词： COVID-19 Deep learning Training Computed tomography Pulmonary diseases Lung Transformers

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Research on the Method of Emotion Contagion in Virtual Space Based on SIRS 15th

Research on the Method of Emotion Contagion in Virtual Space...

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15th CCF Conference on computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Liu, Heng Hong, Xiao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9789811625398

There is emotional contagion in virtual space, and the process of emotional contagion is also affected by emotional recurrence. However, the existing emotional contagion model does not consider the impact of individual emotional recurrence on emotional contagion in the virtual space. Therefore, the emotional contagion process cannot be simulated. The paper proposes a method of recurrent emotional contagion in virtual space. First, we construct an individual emotional recurrence contagion model based on SIRS (Susceptible-Infected-Recovered-Susceptible), we propose emotional recurrence contagion rules, and consider the impact of emotional recurrence in the virtual space on the emotional contagion process;secondly, we construct a virtual space emotional recurrence contagion model (REC) and solve it to simulate the process of emotional recurrence contagion. Third, we build a WS small-world network based on REC simulation. Provide REC (Recurrent emotional contagion model) simulation process in WS small-world network to verify the rationality of the REC. Finally, the REC is simulated to show the simulation effect of emotional recurrence contagion. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Small-world networks

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Static Virus Spread Algorithm for DNA Sequence Design

arXiv

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arXiv 2023年

作者： Yao, Yao Zhang, Xun Liu, Xin Liu, Yuan Zhang, Xiaokang Zhang, Qiang The School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian116622 China

DNA is not only the genetic material of life, but also a favorable material for a new computing model. Various research works based on DNA computing have been carried out in recent years. DNA sequence design is the foundation of such research. The sequence quality directly affects the universality, robustness, and stability of DNA computing. How to design DNA sequences depends on the biological properties and target requirements, which is a typical combinatorial optimization problem. In this paper, in order to design DNA sequences with high-quality, we propose a novel meta-heuristic evolutionary algorithm, termed the static virus spread algorithm (SVS). Through this algorithm, we focus on the constraints of universal DNA sequence design and produce a large number of DNA sequences with non-complementarity and small difference in melting temperature as the objectives, and fully considering the balanced proportion of the four bases. The computer simulation and polyacrylamide gel electrophoresis experiments show that the high-quality DNA sequences designed by this algorithm are effective, which is expected to provide a convenient tool for sequence preparation before DNA biochemical operations. Copyright © 2023, The Authors. All rights reserved.

关键词： DNA sequences

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Knowledge transfer for Object Detection with Evolution architecture search 4

Knowledge transfer for Object Detection with Evolution archi...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Li, Jiaquan Hong, Haokai Shi, Minghui Lin, Qiuzhen Zhou, Fenfen Tan, Kay Chen Jiang, Min Xiamen University School of Informatics Fujian China Shenzhen University College of Computer Science and Software Engineering Guangdong China Fujian Longjing Environment Protection Co. Ltd Fujian China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Deep learning has been proved to achieve excellent results in various fields, and appropriate network architecture and sufficient data play an important role. Due to the high cost of annotation for the task of object detection, domain adaptation methods have been introduced in this field. But these methods are based on the rigid network architecture and bound to the input dimension of the adaptive module, which is not only difficult to better balance accuracy and speed of detection model, but also can not use the multi-scale training method, resulting in the reduction of the application scenario of the model. Inspired by this problem, we propose a new object detection method based on multi-scale adversarial domain adaptation and network architecture search. An evolutionary algorithm is adopted to help search the network architecture to balance accuracy and speed. The ability of domain adaptation can also be effectively improved by the searched architecture. The experimental results have demonstrated the significant improvement that benefited from the framework in terms of its performance and computational efficiency in solving unlabeled object detection. © 2022 IEEE.

关键词： Network architecture

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Succinct quantum testers for closeness and k-wise uniformity of probability distributions

arXiv

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arXiv 2023年

作者： Luo, Jingquan Wang, Qisheng Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Graduate School of Mathematics Nagoya University Nagoya464-8602 Japan Shenzhen518045 China

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and k-wise uniformity of probability distributions. • Closeness testing is the problem of distinguishing whether two n-dimensional distributions are identical or at least Ε-far in 1- or 2-distance. We show that the quantum query complexities for 1- and 2-closeness testing are O(√n/Ε) and O(1/Ε), respectively, both of which achieve optimal dependence on Ε, improving the prior best results of Gilyén and Li (2020). • k-wise uniformity testing is the problem of distinguishing whether a distribution over {0, 1}n is uniform when restricted to any k coordinates or Ε-far from any such distribution. We propose the first quantum algorithm for this problem with query complexity O(√nk/Ε), achieving a quadratic speedup over the state-of-the-art classical algorithm with sample complexity O(nk/Ε2) by O'Donnell and Zhao (2018). Moreover, when k = 2 our quantum algorithm outperforms any classical one because of the classical lower bound Ω(n/Ε2). All our quantum algorithms are fairly simple and time-efficient, using only basic quantum subroutines such as amplitude estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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LOGCAN++: Adaptive Local-Global Class-Aware Network for Semantic Segmentation of Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Ma, Xiaowen Lian, Rongrong Wu, Zhenkai Guo, Hongbo Yang, Fan Ma, Mengting Wu, Sensen Du, Zhenhong Zhang, Wei Song, Siyang Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200030 China Zhejiang University School of Computer Science and Technology Hangzhou310027 China Zhejiang University School of Earth Sciences Hangzhou310027 China Zhejiang University School of Software Technology Hangzhou310027 China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang Jiaxing314103 China University of Exeter HUBG Laboratory Department of Computer Science ExeterEX4 4PY United Kingdom

Remote sensing images are usually characterized by complex backgrounds, scale and orientation variations, and large intraclass variance. General semantic segmentation methods usually fail to fully investigate the above issues, and thus their performances on remote sensing image segmentation are limited. In this article, we propose our LOGCAN++, a semantic segmentation model customized for remote sensing images, which is made up of a global class-aware (GCA) module and several local class-aware (LCA) modules. The GCA module captures global representations for class-level context modeling to reduce the interference of background noise. The LCA module generates local class representations as intermediate perceptual elements to indirectly associate pixels with the global class representations, targeting dealing with the large intraclass variance problem. In particular, we introduce affine transformations in the LCA module for adaptive extraction of local class representations to effectively tolerate scale and orientation variations in remote sensing images. Extensive experiments on three benchmark datasets show that our LOGCAN++ outperforms current mainstream general and remote sensing semantic segmentation methods and achieves a better trade-off between speed and accuracy. © 1980-2012 IEEE.

关键词： Semantic Segmentation

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A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud

arXiv

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arXiv 2023年

作者： Liu, Wenjie Zhang, Ying Deng, Zhiliang Zhao, Jiaojiao Tong, Lian Nanjing University of Information Science & Technology School of Computer and Software Ningliu Road Nanjing210044 China Nanjing University of Information Science & Technology School of Automation Ningliu Road Nanjing210044 China School of Information Engineering Jiangsu Maritime Institute Nanjing211100 China

As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human-computer interaction computing mode in cloud. The purpose of stabilizing the generation process and realizing the interaction between human and computing process is achieved by inputting artificial conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the"input bottleneck" of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks. © 2023, CC BY.

关键词： Generative adversarial networks

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Plant Diseases Detection and Soil Quality Identification Techniques for Agriculture Professionals

Plant Diseases Detection and Soil Quality Identification Tec...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： M. A. Y. Peer Mohamed Appa V. Ganesh Karthikeyan Tasneem K. H. Khan R. Vaishnavi Karthika D. Vijaya Kumar S. P. Ramesh R. Saravanakumar Department of Artificial Intelligence and Machine Learning School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India School of Computing SASTRA Deemed to be University Thirumalaisamudram Thanjavur Tamil Nadu India Department of Chemistry Anjuman College of Engineering & Technology Sadar Nagpur India Department of Computer Science and Engineering VSB College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Human civilization requires food production. Plant leaves and harvesting illnesses affected scientists' soil quality assessments. Plagues slow species growth. The right disease classification may reduce plant damage. Many diseases are hard to classify. Farmers misinterpret soil and plant leaf diseases. Hand-made soil quality and pathogen estimates are difficult. Without soil quality monitoring and plant disease classification, agricultural goods might be severely destroyed. Image processing may boost agricultural yield. Several methods utilize images of damaged leaves or crops to diagnose plant diseases and soil quality. Research has improved sickness categorization and diagnosis in several ways. Recent statements, actions, and debates need more. Global farming uses technology. CNN and ANN are artificial neural networks. Deep or traditional Machine Learning (ML) relies on the issue, data, and computing power. When processing and data resources are available, deep learning (DL), which uses convolutional neural networks (CNNs) and artificial neural networks (ANNs), is used for several challenging visual identification and classification tasks. It excels on its own datasets but struggles on others. This study's data, assessment criteria, and conclusions will help plant leaf and crop detection and soil quality measuring researchers.

关键词： Deep learning Plant diseases Visualization Machine learning algorithms Crops Soil Feature extraction Classification algorithms Convolutional neural networks Diseases

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Dynamic Power Distribution Controlling for Directional Chargers

Dynamic Power Distribution Controlling for Directional Charg...

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

作者： Yuzhuo Ma Dié Wu Jing Gao Wen Sun Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Cyberspace Security Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor Networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

关键词： Wireless communication Wireless sensor networks Heuristic algorithms Computational modeling Power distribution Interference Search problems

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