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16th International Conference on Intelligent Computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Provably Convergent Federated Trilevel Learning

arXiv

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data

Semantic Map Learning of Traffic Light to Lane Assignment ba...

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International Conference on Intelligent Transportation

作者： Thomas Monninger Andreas Weber Steffen Staab Mercedes-Benz Research & Development North America Sunnyvale CA USA Mercedes-Benz AG Research & Development Stuttgart Germany University of Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany Electronics and Computer Science University of Southampton Southampton United Kingdom

Understanding which traffic light controls which lane is crucial to navigate intersections safely. Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes. The manual provisioning of this information is tedious, expensive, and not scalable. To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic. This works in an automated way and independently of the geometric arrangement. We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method. In addition, our novel rejection method includes accompanying safety considerations by leveraging statistical hypothesis testing. Finally, we propose a dataset transformation to re-purpose available motion prediction datasets for semantic map learning. Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches 5 5 https://***/map-learning/tl2la.

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Destination Scheduling for Secure Pinhole-Based Power-Line Communication

arXiv

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arXiv 2023年

作者： Kundu, Chinmoy Dubey, Ankit Tonello, Andrea M. Nallanathan, Arumugam Flanagan, Mark F. School of Electrical and Electronic Engineering University College Dublin Belfield Ireland Department of EE Indian Institute of Technology Jammu Jammu & Kashmir India Institute of Networked and Embedded Systems University of Klagenfurt Austria School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a powerline communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pinhole (PH) architecture for a multi-node PLC network to capture the keyhole effect in PLC. The transmitter-to-PH link is shared between the destinations and an eavesdropper which correlates all end-to-end links. The individual channel gains are assumed to follow independent log-normal statistics. Furthermore, the additive impulsive noise at each node is modeled by an independent Bernoulli-Gaussian process. Exact computable expressions for the average secrecy capacity (ASC) and the probability of intercept (POI) performance over many different networks are derived. Approximate closed-form expressions for the asymptotic ASC and POI are also provided. We find that the asymptotic ASC saturates to a constant level as transmit power increases. We observe that the PH has an adverse effect on the ASC. Although the shared link affects the ASC, it has no effect on the POI. We show that by artificially controlling the impulsive to background noise power ratio and its arrival rate at the receivers, the secrecy performance can be improved. © 2023, CC BY.

关键词： Impulse noise

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Scheduling Multi-Server Jobs with Sublinear Regrets via Online Learning

arXiv

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arXiv 2023年

作者： Zhao, Hailiang Deng, Shuiguang Xiang, Zhengzhe Yan, Xueqiang Yin, Jianwei Dustdar, Schahram Zomaya, Albert Y. Hainan Institute of Zhejiang University Sanya572025 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The School of Computer Science and Technology Hangzhou City University Hangzhou310015 China Huawei Technologies Co Ltd Shanghai201206 China The Distributed Systems Group Technische Universität Wien Vienna1040 Austria The School of Computer Science University of Sydney SydneyNSW2006 Australia

Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal communication overhead, apart from the resource contention between jobs. To study the computation-communication tradeoff, we model the computation gain as the speedup on the job completion time when it is executed in parallelism on multiple computing instances, and fit it with utilities of different concavities. Meanwhile, we take the dominant communication overhead as the penalty to be subtracted. To achieve a better gain-overhead tradeoff, we formulate an cumulative reward maximization program and design an online algorithm, named OGASCHED, to schedule multi-server jobs. OGASCHED allocates the multi-type resources to each arrived job in the ascending direction of the reward gradients. It has several parallel sub-procedures to accelerate its computation, which greatly reduces the complexity. We proved that it has a sublinear regret with general concave rewards. We also conduct extensive trace-driven simulations to validate the performance of OGASCHED. The results demonstrate that OGASCHED outperforms widely used heuristics by 11.33%, 7.75%, 13.89%, and 13.44%, respectively. © 2023, CC BY-NC-ND.

关键词： E-learning

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Towards self-explainable cyber-physical systems

arXiv

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arXiv 2019年

作者： Blumreiter, Mathias Greenyer, Joel Garcia, Francisco Javier Chiyah Klös, Verena Schwammberger, Maike Sommer, Christoph Vogelsang, Andreas Wortmannz, Andreas Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universität Hannover Germany Heriot-Watt University Software and Embedded Systems Engineering Technische Universität Berlin Germany Department of Computing Science University of Oldenburg Heinz Nixdorf Institute Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universität Berlin Germany Software Engineering RWTH Aachen University

With the increasing complexity of Cyber-Physical systems, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems that leverage requirements- A nd explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet unexplainable behavior be detected by the system. Copyright © 2019, The Authors. All rights reserved.

关键词： embedded systems

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Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data

arXiv

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arXiv 2023年

作者： Monninger, Thomas Weber, Andreas Staab, Steffen Mercedes-Benz Research & Development North America SunnyvaleCA United States Mercedes-Benz AG Research & Development Stuttgart Germany University of Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany University of Southampton Electronics and Computer Science Southampton United Kingdom

关键词： Semantics

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Autonomous Underwater Vehicle trajectory design coupled with predictive ocean models: A case study

Autonomous Underwater Vehicle trajectory design coupled with...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ryan N. Smith Arvind Pereira Yi Chao Peggy P. Li David A. Caron Burton H. Jones Gaurav S. Sukhatme Department of Computer Science Robotic Embedded Systems Laboratory University of Southern California Los Angeles CA USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Marine Biology University of Southern California Los Angeles CA USA

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceanographic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to maneuver through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.

关键词： Underwater vehicles Trajectory Oceans Predictive models Intelligent sensors Sea measurements Path planning Strategic planning Navigation Marine vehicles

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A Qualitative Measurement Survey of Popular Internet-based IPTV systems

A Qualitative Measurement Survey of Popular Internet-based I...

引用

Second International Conference on Communications and Electronics

作者： Tobias Hossfeld Kenji Leibnitz Institute of Computer Science Department of Distributed Systems University of Würzburg Germany Graduate School of Information Science and Technology Osaka University Japan

Television over the Internet (IPTV) is regarded as the killer application for Next Generation Networks (NGN). While the ITU is currently moving toward the standardized of IPTV, several existing applications like Joost and Zattoo are currently already providing a large amount of video content to Internet users. In this paper we study some popular video content delivery mechanisms and characterize them by measurements to study the user-perceived Quality of Experience (QoE).

关键词： IPTV Web server Satellite broadcasting Network servers TV broadcasting Internet telephony IP networks Next generation networking Digital video broadcasting Web and internet services

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Extended Empty-Slots Method for fixed Priority Feasibility Analysis

Extended Empty-Slots Method for fixed Priority Feasibility A...

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2010 2nd International Conference on computer Engineering and Technology(2010年第二届计算机工程与技术国际会议 ICCET 2010)

作者： Li Jin Nasro Min-Allah Jian-Sheng Xing Xiu-Li WANG The Missile Inst. Air Force Engineering Univ. Sanyuan 713800 China COMSATS Institute of Information Technology Department of Computer ScienceIslamabad 44000Pakistan Embedded Systems Institute 5600 MB Eindhoventhe Netherlands School of information Central University of Finance and EconomicsBeijing 100081China

ISBN: (纸本)9781424463473

For preemptive scheduling,the discrete scheduling has the same capability as the continuous scheduling. However,most work previously done has been focused on the continuous scheduling. In this paper,a discrete analysis approach Empty Slots method is discussed and applied to fixed priority scheduling for constraint-deadline task model. Then a new schedulability test using the Empty-Slots method is presented. Compared with classical schedulability tests for continuous scheduling,this test is more convenient to be used and more efficient to tackle practical problems.

关键词： real-time system discrete scheduling:fixed priority scheduling:Empty-Slots method:schedulability test

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