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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation

Multi-scale cyclical similarity prototype refinement for few...

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international conference on Signal Processing Proceedings (ICSP)

作者： Yingfeng Ou Xing Yang Jian Zhang Caiqing Jian Lihui Wang Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%.

关键词： Image segmentation Ultrasonic imaging Breast tumors Semantics Prototypes Signal processing Data mining Few shot learning Residual neural networks

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Multiple Task-Oriented intelligent Optimization of Computation, Communication and Caching Resource in MEC-Enabled Heterogeneous Networks

Multiple Task-Oriented Intelligent Optimization of Computati...

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international conference on Communication technology (ICCT)

作者： Yuan Zhi Xiaona Jiang Jie Tian Chenxu Huang Tiantian Li School of Information Science and Engineering Shandong Normal University Jinan China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Mobile edge computing and caching technologies are emerged as promising paradigm for reducing the task computing delay and content acquisition delay in heterogeneous networks. However, due to the limited communication, computation and caching (3C) resources, how to jointly optimize the 3C resources to guarantee the heterogeneous quality of service (QoS) for different types of tasks is still a challenge. In this paper, we aim to minimize the sum of cost of all the users with different types of tasks in each small cell by jointly designing computation offloading, resource allocation strategies for computation-request tasks and cooperative edge caching strategies for content-request tasks in heterogeneous networks. To this end, we propose a multi-agent deep deterministic policy gradient (MADDPG)-based multi-dimensional resources optimization algorithm with centralized training and distributed execution. Simulation results show that the proposed scheme can achieve better performance compared with other baseline schemes.

关键词： Training Costs Simulation Microprocessors Computer architecture Quality of service Heterogeneous networks Delays Resource management Optimization

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Micro Loans for Farmers

Micro Loans for Farmers

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international conference on computing and Networking technology (ICCNT)

作者： Rushabh Khara Deepika Pomendkar Rishika Gupta Ishwarlal Hingorani Dhananjay Kalbande Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

As per 2018, agriculture provides employment to more than 50% of the Indian workforce and contributes 17-18% to the country's GDP. Every farmer possesses a requirement of credibility or other services during the time of cultivation. Due to unavailability of bank loans for low-income farmers, they have no other option but to look up to private lenders or micro-finance institutions. the interest rate charged by microfinance institutions consists of various subjective factors which forces MFIs to provide microcredits to farmers at high-interest rates. Relevant to this context, this paper proposes a blockchain-based system which eliminates the need of MFIs. the system handles microloans on a distributed ledger which increases transparency among stakeholders and also enables small/large scale investors to lend microcredits to farmers at interest rate and repayment schedule decided by the farmer. the proposed system facilitates low-cost, secure and real-time payments with the help of smart contracts.

关键词： Economic indicators Blockchain Agriculture Sustainable development Stakeholders Schedules Real-time systems

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Visual Analysis of the Bibliometric Data Associated with the Calibration of Car-Following Models

Visual Analysis of the Bibliometric Data Associated with the...

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international conference on distributed computing in Sensor Systems (DCOSS)

作者： Mădălin-Dorin Pop Mihai V. Micea Computer and Information Technology Department Politehnica University of Timişoara Timişoara România

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

intelligent transportation systems (ITS) use the latest technologies for real-time traffic control and monitoring to ensure efficient traffic management and reduce the risks of traffic accidents. the microscopic level of traffic modeling is the most appropriate level for controlling and monitoring the interaction between vehicles based on car-following scenarios. However, the data retrieved from sensor networks can be affected by measurement errors, and consequently the implementation of appropriate mechanisms to overcome their propagation to the control system is mandatory. this paper aims to analyse the current research in the calibration of car-following models and provide valuable insights of recent developments in this field. To achieve this goal, VOSviewer has been chosen as a visualisation tool to create bibliographic maps based on the output from the well-known scientific database Clarivate Analytics Web of Science (WoS). the maps obtained provide a visual representation of the main institutions involved in this field of research and identify the research interests based on author and indexing keywords. Furthermore, this paper analyses the top five clusters identified based on the analysis of co-occurrence keywords, presenting discussions about the connections existing within these clusters.

关键词： Analytical models Visualization Computational modeling Bibliometrics Transportation Traffic control Data models

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Optimal Dispatch of Energy Resources in an Isolated Micro-Grid with Battery Energy Storage System

Optimal Dispatch of Energy Resources in an Isolated Micro-Gr...

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international conference on ent computing and Control Systems (ICICCS)

作者： P. Krishna Mohan Reddy M. Prakash Department of Electrical and Electronics Engineering National Institute of Technology Nagaland Chumukedima Nagaland India

ISBN: (数字)9781728148762

ISBN: (纸本)9781728148779

With the demise of conventional fossil fuels and increased pollution rates, the intermittent renewables are considered suitable alternates. the erratic nature of loads in the distribution grid further affects the reliability of power systems. Micro-grid with interconnected loads and distributed generation will be beneficial for certain areas, where the extension of grid connectivity is not feasible. Further, the advancement of the battery storage system enables the distribution grid to consider it as the source, whenever the power generation is not available. In the present study, a micro-grid with distributed generation and interconnected loads up to 82kW has been considered. the main objective of the present study is to minimize the overall fuel cost by achieving the optimal scheduling of energy sources. Extensive analysis has been carried to achieve the optimal dispatch condition at all the instances while solving the demand criteria. To reduce the fuel cost, several optimization strategies including particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization (GWO), and modified grey wolf optimization (MGWO) have been adopted. For the present test study, the MGWO algorithm has yielded better results when compared with other traditional optimization algorithms. the total cost of microgrid optimized through WOA, GWO, PSO, MGWO is 1455.50 (€/day), 1451.70 (€/day), 1449.87 (€/day), and 1433.60 (€/day) respectively.

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USV Path Planning Under Marine Environment Simulation Using DWA and Safe Reinforcement Learning

USV Path Planning Under Marine Environment Simulation Using ...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Tianci Qu Gang Xiong Hub Ali Xisong Dong Yunjun Han Zhen Shen The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Qingdao Academy of Intelligent Industries Qingdao China

It is a challenge for USV navigation due to uncertainties and disturbances in the complex marine environment, which may lead to tilting or collision. However, current path planning methods for USVs lack dynamic environment adaptivity and timeliness. We formulate this problem as a Markov Decision Process (MDP) and combine the advantages of Dynamic Window Approach (DWA) and Safe Reinforcement Learning (RL) to solve it. First, the state encoding output by DWA is employed as observation to reduce the state dimension. Second, a safety-ensured reward function is designed to avoid collision areas. then, the actor-critic network based on RL algorithm is used. Along with the RL training, the parameters in DWA is adaptively adjusted. Simulation experiments with dynamic waves and ocean currents demonstrate the adaptivity of our method, with avg. trajectory length decreased by 10%, avg. time cost decreased by 17%, and avg. speed increased by 18%, compared to the case using only DWA.

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Clustering botnet behavior using K-means with uncertain data 18

Clustering botnet behavior using K-means with uncertain data

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18th international conference on New Trends in intelligent Software Methodologies, Tools and Techniques, SoMeT 2019

作者： Ibrahim, Wan Nur Hidayah Selamat, Ali Anuar, Syahid Krejcar, Ondrej School of Computing Faculty of Engineering Universiti Teknologi Malaysia Universiti Teknologi Malaysia Johor Baharu Johor81310 Malaysia Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9781643680125

Botnets are the most deadly threat in the network due to the capability of exploiting resources within a network as an army to launch huge attacks such as Denial-distributed-of-Service (DDOS) or spam emails. Network Intrusion Detection System (NIDS) that designed based on the behavior of botnets in network traffic is seen as the promising technique in detecting botnets that are hiding by using encryption technique or any hiding techniques. this paper proposes on K-means clustering algorithm as the first phase of botnet's behaviour detection model that extracts data from network traffic. the criterion highlighted for our behaviour detection model is that it should be able to detect botnet in encrypted packets(hiding techniques), structure-independent (centralized and peer-to-peer), requiring minimal computing resources and minimal time processing. Other than that, by representing the real-time of network traffics, the detection model also must be resistant to noise and able to identify the anomaly of botnets behavior among a huge number of normal traffic. We are using the botnet benchmark dataset and normal traffic from Malware Capture Facility Project and comparing our proposed method using K-means algorithm with Expectation Maximization algorithm that proposed by the previous researcher in clustering the similar pattern of botnet behavior. the result shows that the K-means algorithm producing much higher accuracy, 94% and lower false negative rate, 0.1413. While, average accuracy for Expectation Maximization algorithm is 88% and False Negative Rate, 0.2245 with the insertion of uncertain data from real network traffic. © 2019 the authors and IOS Press. All rights reserved.

关键词： Botnet

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Design of Cloud Intelligence Logistics Management System Based on Big Data

Design of Cloud Intelligence Logistics Management System Bas...

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2018 5th international conference on Electrical & Electronics Engineering and Computer Science(ICEEECS 2018)

作者： Lijuan Ma Lina Gao Xi'an International University

With the increasing integration of the world economy, the traditional logistics distribution mode shows the disadvantages of backward management, low efficiency and unshared information, so it is urgent to have an intelligent logistics management system with high efficiency, convenience, comprehensiveness and decision-making power. In this paper, a cloud intelligence logistics management system based on big data is proposed, which uses big data to optimize decision-making and cloud computing to share computing resources to improve logistics efficiency.

关键词： big data cloud intelligence logistics management system

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Efficient Advance Encryption Standard (AES) Implementation on FPGA Using Xilinx System Generator

Efficient Advance Encryption Standard (AES) Implementation o...

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international conference on intelligent and Advanced Systems

作者： S. M. Umar Talha Mir Asif Hammad Hussain Ali Asghar Hadi Ameen Department of Telecommunication Engineering Sir Syed University of Engineering and Technology Department of Electronics Engineering Sir Syed University of Engineering and Technology

ISBN: (纸本)9781509008469

the paper presents an efficient reconfigurable hardware implementation of Advance Encryption Standard (AES) algorithm on Field Programmable Gate Array (FPGA);using High Level Language (HLL) approach with less hardware resources. the FPGA platform used for AES implementation is Xilinx Atlys Virtex-6. Time-to-market is one of the key factors for any design in FPGA and digital system designing industry. this time can be reduced considerably with HLL approach. the presented algorithm is designed on a HLL tool, namely Xilinx system generator. It is very user friendly despite giving detailed control in designing the required system design. For actual testing and hardware implementation of the algorithm, the HLL-tool generates a bit file that can be directly burnt on the FPGA. To get the implementation of design on hardware, the presented work uses a similar approach to directly map the System Generator described design on FPGA. the presented work emphasizes on optimization for less hardware utilization. the presented design uses approximately just one thousand slices and about half a century of BRAMs. the frequency at which the hardware can work is about 254.453 MHz. the results observed are very promising and the design outperforms previously reported results using HLL approach.

关键词： AES System Generator FPGA Reconfigurable computing

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An Asynchronous Federated Learning Method for Industrial Internet of things Based on Local Differential Privacy

An Asynchronous Federated Learning Method for Industrial Int...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Huiqi Zhao Huajie Zhang Fang Fan Yaowen Ma School of Intelligent Equipment Shandong University of Science and Technology Shandong China

Federated learning (FL) has been considered a promising artificial intelligence technology that aims to provide distributed machine learning services while protecting the privacy of training data on terminal device nodes. the traditional federated learning method obtains a local model through training on different device nodes and completes the update of the global model through a central server. However, this approach has issues such as slow communication and privacy leakage. We propose an asynchronous method for updating aggregation in federated learning. the server effectively manages the weight of each model parameter based on the varying training speeds of device nodes to minimize the impact of slower device nodes. Adopting this method can not only improve the efficiency of model updating but also reduce the demand for network bandwidth and computing resources, making it more suitable for resource-constrained environments such as Industrial Internet of things (IIoT) systems. In addition, to mitigate the impact of poisoned data, we have developed a dynamic mechanism for allocating privacy budget based on the local Rényi Differential Privacy (RDP) mechanism. this mechanism adjusts the size of the privacy budget to achieve a better balance between model efficiency and privacy protection in the context of the Industrial Internet of things.

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