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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Shuyue Ai Yuna Jiang Qiang Li Xiaohu Ge Aduwati Sali School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Dept. of Computer and Communication Systems Engineering Faculty of EngineeringUPM WiPNET Research Centre

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The dynamic spectrum sharing technology in cognitive radio can effectively improve the utilization rate of spectrum and relieve the current spectrum pressure. To realize spectrum sharing without trust between SUs and PUs belonging to various operators, a decentralized spectrum sharing scheme based on blockchain is proposed in this paper. A latency model and a decentralization degree model of blockchain-enabled spectrum sharing network are formulated. Network scale law in this paper is defined as the change law of structure and scale in a network. Based on the proposed decentralization degree and latency model, the scale law of blockchain networks under the constraints of latency and decentralization is studied. The simulation analyzes the change of the proportion of consensus nodes in the blockchain network with different requirements for latency and decentralization. The results show that when blockchain networks have both low latency and decentralization characteristics, the upper limit of the total number of nodes is 390, and the value range of the proportion of consensus nodes is between 0.13 and 0.56.

关键词： Blockchains Cognitive radio Low latency communication Mobile computing

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SPARROW: Smart Precision Agriculture Robot for Ridding of Weeds

arXiv

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arXiv 2024年

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Arachchige, Gayantha Godakanda Bandara, Anuththara Wellalage, Sasini Pandithage, Dinithi Hansika, Mahaadikara M.D.J.T. de Silva, Rajitha Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka City of Melbourne Australia Lincoln Agri-Robotics University of Lincoln United Kingdom

The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. This proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds. © 2024, CC BY.

关键词： Machine learning

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Authentic Learning Approach for Artificial Intelligence systems Security and Privacy

Authentic Learning Approach for Artificial Intelligence Syst...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Mst Shapna Akter Hossain Shahriar Dan Lo Nazmus Sakib Kai Qian Michael Whitman Fan Wu Dept. of Computer Science Kennesaw State University Kennesaw USA Dept. of Information Technology Kennesaw State University Kennesaw USA Dept. Computer Science Kennesaw State University Kennesaw State University USA Dept. of Information Systems and Security Kennesaw State University Kennesaw USA Dept of Computer Science Tuskegee University Tuskegee USA

The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.

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Medical Knowledge-Guided Deep Learning for Mammographic Breast Density Classification

Medical Knowledge-Guided Deep Learning for Mammographic Brea...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Gao, Long Liu, Chang Arefan, Dooman Panigrahy, Ashok Zuley, Margarita L. Wu, Shandong Dept. of Radiology University of Pittsburgh PittsburghPA15213 United States Dept. of Bioengineering University of Pittsburgh PittsburghPA15213 United States Dept. of Biomedical Informatics University of Pittsburgh PittsburghPA15213 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15213 United States College of Computer National University of Defense Technology Changsha410073 China Magee-Womens Hospital University of Pittsburgh Medical Center PittsburghPA15213 United States

ISBN: (纸本)9781510660359

Classification of Breast Imaging Reporting and Data System (BI-RADS) breast density categories generally reflects the amount of dense/fibroglandular tissue in the breast. Studies have consistently shown that breast with higher density has a higher risk of developing breast cancer compared to breast with lower density. In this paper, we propose a novel end-to-end method, namely, Medical Knowledge-guided Deep Learning (MKDL), for breast mammogram density classification. The principle behind MKDL lies in the fact that many breast image density classification tasks are partly or largely based on certain pre-known image features, such as image contrast and brightness. These pre-known features can be computationally represented and then leveraged as prior knowledge to facilitate more effective model learning and thus boost the classification performance. We designed specific knowledge-based transformations for breast density classification and showed that our model outperformed several state-of-the-art models. © 2023 SPIE.

关键词： Classification (of information)

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Online Task Scheduling Algorithm with Complex Dependencies in Edge Computing 12

Online Task Scheduling Algorithm with Complex Dependencies i...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Shi, Lei Ma, Zhaoxing Fan, Yuqi Shi, Yi Ding, Xu Li, Zhehao School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Procince Hefei University of Technology Hefei 230009 China Virginia Tech Dept. of ECE BlacksburgVA24061 United States

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. © 2022 The Authors. Published by Elsevier B.V.

关键词： Dependent task Edge computing Online scheduling

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Understanding Statistical Correlation of Application Security Vulnerability Data from Detection and Monitoring Tools

Understanding Statistical Correlation of Application Securit...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Santanam Kasturi Xiaolong Li John Pickard Peng Li Technology Management Indiana State University Terre Haute USA Dept. of Electronics and Computer Engineering Indiana State University Terre Haute USA Dept. of Technology Sys East Carolina University Greenville USA Dept. of Technology Systems East Carolina University Greenville USA

Vulnerability data gathered from multiple detection and monitoring capabilities at different layers of an application using a time-series analysis will provide value and insights by doing a statistical correlation with attack requests observed using a Web Application Firewall (WAF) monitoring solution. Static Analysis Security Testing, Software Composition Analysis, Dynamic Analysis Security Testing (DAST), Application Ethical Hack, Application Programming Interface (API) testing are the tests / scans that have been used to gather vulnerability data for this study. Correlations can further help track abnormal transaction paths if we follow specific ones pointed out by the statistical analysis for those requests that are not blocked by the rules and are allowed as valid transactions to pass through. This provides a narrowed down focus on the convergence of observability and security, critical to realizing a near-real time rapid action. Observations must continue for many days as a time series ensuring consistency and reliability in data collections and analysis. Multiple applications must be observed in a similar manner for ensuring validity of the process for analysis. Also, gathering sufficient data that is large enough to represent a reasonable population of web applications within an organization is a significant factor in achieving reliable correlation. Applying Pearson Correlation (or Spearman Correlation for distributions that are not normal) technique provides insight into Significance (two-tailed) as to whether a correlation is present over large number of data points. Results of analysis show evidence of correlations among specific attack requests monitored by the WAF and corresponding vulnerabilities in applications, detected using one or more methods. This is significant to looking for more insights into how these correlations can further explored into predicting attack patterns based on existing vulnerabilities.

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Maximum Length Rll Sequences in De Bruijn Graph

SSRN

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SSRN 2024年

作者： Etzion, Tuvi Chee, Yeow Meng Nguyen, Tien Long Ta, Duy Hoang Tran, Vinh Duc Vu, Van Khu Dept. of Industrial Systems Engineering and Management National University of Singapore Singapore Computer Science Faculty Technion Israel Institute of Technology Haifa3200003 Israel Hanoi University of Science and Technology Viet Nam

To avoid a long period of no-pulse in a synchronized system for free-space quantum key distribution, a system based on a de Bruijn sequence was *** this system on-off pulse is used to simulate a \emph{zero} and on-on pulse is used to simulate a \emph{one}.However, these proposed sequences have high redundancy and low rate. To reduce the redundancy and increase the rate,run-length limited sequences in the de Bruijn graph are proposed in this work for the same *** maximum length of such sequences in the de Bruijn graph is studied and an efficient algorithm to construct a large set of these sequencesis presented. Based on known methods,an enumeration for the number of maximum length sequences is given. © 2024, The Authors. All rights reserved.

关键词： Quantum cryptography

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Maximum Length RLL Sequences in de Bruijn Graph

arXiv

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arXiv 2024年

作者： Chee, Yeow Meng Etzion, Tuvi Nguyen, Tien Long Ta, Duy Hoang Tran, Vinh Duc Vu, Van Khu Dept. of Industrial Systems Engineering and Management National University of Singapore Singapore Computer Science Faculty Technion Israel Institute of Technology Haifa3200003 Israel Hanoi University of Science and Technology Viet Nam

Free-space quantum key distribution requires to synchronize the transmitted and received signals. A timing and synchronization system for this purpose based on a de Bruijn sequence has been proposed and studied recently for a channel associated with quantum communication that requires reliable synchronization. To avoid a long period of no-pulse in such a system on-off pulses are used to simulate a zero and on-on pulses are used to simulate a one. However, these sequences have high redundancy and low rate. To reduce the redundancy and increase the rate, run-length limited sequences in the de Bruijn graph are proposed for the same purpose. The maximum length of such sequences in the de Bruijn graph is studied and an efficient algorithm to construct a large set of these sequences is presented. Based on known algorithms and enumeration methods, maximum length sequence for which the position of each window can be computed efficiently is presented and an enumeration on the number of such sequences is given. © 2024, CC BY.

关键词： Synchronization

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Strategies for Optimizing Time Series Visual Data Exploration

Strategies for Optimizing Time Series Visual Data Exploratio...

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ACS/IEEE International Conference on computer systems and Applications

作者： Heba Helal Mohamed A. Sharaf College of Information Technology United Arab Emirates University UAE Dept. of Computer Engineering and Systems College of Engineering Mansoura University Egypt

Dealing with high-dimensional time series data makes the process of recommending visualizations with "interesting" insights difficult. The challenge originates from finding a way to obtain the recommended visualizations efficiently without compromising their quality. Identifying such visualizations manually is considered a labor-intensive and time-consuming process. In response, this paper introduces different techniques designed to optimize the automated recommendation process. These techniques are entirely based on the concept of computation sharing and pruning. Furthermore, we provide a glimpse into our future research works in PhD thesis. The objective is to broaden the scope of our current work and enhance the generality of our problem statement.

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Multi-agent and Reinforcement Learning Schemes for Demand Response Estimation in Distributed Microgrids

Multi-agent and Reinforcement Learning Schemes for Demand Re...

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International Conference on Energy Conservation and Efficiency (ICECE)

作者： Muhammad Ikram Salman Ahmed Safdar Nawaz Khan Marwat Muhammad Nasir Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Physics Allama Iqbal Open University Islamabad Pakistan

Demand response (DR) is an approach that encourages consumers to shape consumption patterns in peak demands for the reliability of the power system and cost minimization. The optimal DR scheme has not only leverages the distribution system operators (DSOs) but also the consumers in the energy network. This paper introduced a multi-agent coordination control and reinforcement learning approach for optimal DR management. Each microgrid is considered an agent for the state and action estimation in the smart grid and programmed rewards and incentive plans. In this regard, the Multi-agent Markov game (MAMG) is utilized for the state and action. At the same time, the reward is articulated through reinforcement learning deep Q-network (DQN) and deep deterministic policy gradient (DDPG) schemes. The proposed DR model also encourages consumer participation for long-term incentivized benefits through integrating battery energy storage systems (BESS) in the SG network. The reliability of DQN and DDPG schemes is demonstrated and observed that the dynamically changing electricity cost is reduced by 19.86%. Moreover, the controllability of complex microgrids is achieved with limited control information to ensure the integrity and reliability of the network. The proposed schemes were simulated and evaluated in MATLAB and Python (PyCharm IDE) environments.

关键词： Costs Estimation Microgrids Reinforcement learning Mathematical models Power system reliability Smart grids

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