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International Conference on Data engineering

作者： Yu Chen Sheng Zhang Ziying Zhou Xiaokun Wang Yu Liang Ning Chen Yuting Yan Mingjun Xiao Jie Wu Zhuzhong Qian Harry Xu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Systems Science Beijing Normal University China School of Computer and Electronic Information / School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China P.R. China Center for Networked Computing Temple University USA Computer Science Department Samueli School of Engineering University of California Los Angeles USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

关键词： Crowdsourcing Accuracy Visual analytics Games Data engineering Outsourcing Task analysis

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Chances and Challenges of Chatgpt and Similar Models for Education in M&S 23

Chances and Challenges of Chatgpt and Similar Models for Edu...

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Proceedings of the Winter Simulation Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz The MITRE Corporation Modeling and Analysis Innovation Center Charlottesville USA L3Harris Corporation Mission Analysis Group Herndon USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta USA University of Central Florida School of Modeling Simulation and Training Orlando USA University of Virgina Department of Systems and Information Engineering Charlottesville USA Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn USA

ISBN: (纸本)9798350369663

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within Modeling and Simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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2023 Winter Simulation Conference, WSC 2023

作者： Tolk, Andreas Barry, Philip Loper, Margaret L. Rabadi, Ghaith Scherer, William T. Yilmaz, Levent The Mitre Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

ISBN: (纸本)9798350369663

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

引用

TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Learning analytics summer institute Spain 2020: Learning analytics. time for adoption?

Learning analytics summer institute Spain 2020: Learning ana...

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2020 Learning Analytics Summer Institute Spain: Learning Analytics. Time for Adoption?, LASI-SPAIN 2020

作者： Martínez-Monés, Alejandra Álvarez, Aihnoa Caeiro, Manuel Dimitriadis, Yannis Department of Computer Science Universidad de Valladolid Spain Department of Computer Languages and Systems Universidad Del País Vasco UPV/EHU Spain Department of Telematics Engineering Universidade de Vigo Spain Department of Theory of Signal and Communications and Telematics Engineering Universidad de Valladolid Spain

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Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

arXiv

引用

arXiv 2023年

作者： Sun, Geng He, Long Sun, Zemin Wu, Qingqing Liang, Shuang Li, Jiahui Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog computing

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Smart Water Flow Monitoring and Theft Detection System using IoT

Smart Water Flow Monitoring and Theft Detection System using...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： Varakumari Samudrala Ajay Reddy Yeruva N Jayapal T. Vijayakumar M. Rajkumar Shaik Razia Department of Electronics and Communication Engineering NRI Institute of Technology (A) Agiripalli Andhra Pradesh India Department of IP Engineering Senior Software Engineer Ritchie Bros. Auctioneers Pleasanton CA USA Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India School of Information Technology & Engineering VIT University Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9781665460859

Water is an essential and crucial resource in the world. Without water, there will not be any organisms, flora, and fauna on the planet. Water is most important for living organisms like humans, plants, and animals. Earth is covered by 71% of water bodies and 29% is covered by land. The human body is made up of nearly 70% of water. There is no life when there is no water. So, people should not waste water and use it accordingly. For domestic use, people store the water in their home tank which flows from the corporate tank or in a bore well. Either of the two there will be a water flow when the home tank is filled which leads to water wastage and there will also be a wastage of water when there is a leakage of water or theft of water during the flow of water to the home tank. To control and avoid the overflow of water, a smart water flow monitoring system and theft detection are implemented by using the Internet of Things (IoT). Sensors, NodeMCU, Solenoid valve, and Global System for Mobile communication (GSM) are used to implement this system. When there is a flow above the threshold value and leakage or theft, the cloud will send the alert message to the user.

关键词： GSM Cloud computing Substations Microcontrollers Valves Sensor systems Internet of Things

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UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper

arXiv

引用

arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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