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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Liu, Hang Xia, Yunni Wu, Lei Chen, Peng Software Theory and Technology Chongqing Key Lab Chongqing University Chongqing China School of Mathematics University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9781728168531

Recently, the Cloud Computing paradigm is becoming increasingly popular in supporting large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service (QoS), attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations, e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge and thus they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real-time. To address this problem, we propose a novel Reinforcement-Learning-Based algorithm to multi-workflow scheduling over IaaS clouds. The proposed algorithm aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. In the experiment, our algorithm is evaluated for famous scientific workflow templates and real-world industrial IaaS cloud platforms by a simulation process and we compare our algorithm to the current state-of-the-art heuristic algorithms, e.g., NSGA-II, MOPSO, GTBGA. The result shows that our algorithm performs better than compared algorithm. © 2020 IEEE.

关键词： Multi agent systems

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Secure AI and Blockchain-enabled Framework in Smart Vehicular Networks

Secure AI and Blockchain-enabled Framework in Smart Vehicula...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Rabieinejad, Elnaz Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam K.N.Toosi University of Technology Information Technology Department Tehran Iran University of Guelph Cyber Science Lab School of Computer Science Ontario Canada Kennesaw State University College of Computing and Software Engineering GA United States Brandon University Department of Mathematics and Computer Science Manitoba Canada

ISBN: (纸本)9781665423908

In recent years, Internet of Things (IoT) devices such as drones, smartphones, and smart vehicles have increased, and Smart Vehicular Networks (SVNs) have formed. SVN are one of the vital components in smart cities and improves their functionality and efficiency. Despite the many applications that SVN have shown, they have security vulnerabilities issues, and there are many reports of them being hacked. Most attacks on SVN occur due to wireless communications and information sharing between vehicles. Also, due to the communication among vehicles, failures can spread from the victim device to the entire network and cause widespread damages. SVN features include mobility, decentralization, resource constraints that make traditional security solutions unsuitable for it. We propose a secure framework using blockchain and Deep Neural networks (DNN) to address these challenges. In this framework, we consider cluster-based architecture that vehicles in each cluster can securely communicate using the blockchain. Also, DNN is adopted to detect abnormal vehicles that have been attacked using their network traffic analysis in each zone. We assess blockchain impacts on throughput using different miners with multiple block sizes in our proposed framework. The experimental result indicates the throughput improvement with an increase in miners and block size. In addition, we evaluated the DNN performance for abnormal vehicle detection, which represents 99.82% in terms of accuracy. © 2021 IEEE.

关键词： Deep neural networks

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal Networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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Frontiers of Robotics and software engineering (FRSE), International Conference on

作者： Dun Cao Renhua Dai Tianyu Zhao Fayez Alqahtani Amr Tolba R. Simon Sherratt Min Zhu School of Computer and Communications Engineering Changsha University of Science and Technology Changsha Hunan China Key Lab of Broadcast Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia School of Biomedical Engineering The University of Reading United Kingdom College of Information Science and Technology Zhejiang Shuren University Zhejiang China

Though introducing the Region Proposal Network (RPN) from object detection enabled Siamese trackers’ success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance.

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Double-Dot Network for antipodal grasp detection

arXiv

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arXiv 2021年

作者： Wang, Yao Zheng, Yangtao Gao, Boyang Di Huang The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China The Geometry Robotics

This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net). It follows the recent anchor-free object detection framework, which does not depend on empirically pre-set anchors and thus allows more generalized and flexible prediction on unseen objects. Specifically, unlike the widely used 5-dimensional rectangle, the gripper configuration is defined as a pair of fingertips. An effective CNN architecture is introduced to localize such fingertips, and with the help of auxiliary centers for refinement, it accurately and robustly infers grasp candidates. Additionally, we design a specialized loss function to measure the quality of grasps, and in contrast to the IoU scores of bounding boxes adopted in object detection, it is more consistent to the grasp detection task. Both the simulation and robotic experiments are executed and state of the art accuracies are achieved, showing that DD-Net is superior to the counterparts in handling unseen objects. © 2021, CC BY.

关键词： Object recognition

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Training, testing and benchmarking medical AI models using Clinical AIBench

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第1期2卷 100037-100037页

作者： Huang, Yunyou Miao, Xiuxia Zhang, Ruchang Ma, Li Liu, Wenjing Zhang, Fan Guan, Xianglong Liang, Xiaoshuang Lu, Xiangjiang Tang, Suqing Zhang, Zhifei Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China Faculty of Education Guangxi Normal University Guilin China International Open Benchmark Council Beijing China

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer's disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://***/clinical_aibench/. © 2022 The Authors

关键词： COVID-19

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Spatial Constraint for Efficient Semi-Supervised Video Object Segmentation

SSRN

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SSRN 2023年

作者： Chen, Yadang Ji, Chuanjun Yang, Zhi-Xin Wu, Enhua Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China Beijing100190 China

Semi-supervised video object segmentation is the process of tracking and segmenting objects in a video sequence based on annotated masks for one or more frames. Recently, memory-based methods have attracted a significant amount of attention due to their strong performance. Having too much redundant information stored in memory, however, makes such methods inefficient and inaccurate. Moreover, a global matching strategy is usually used for memory reading, so these methods are susceptible to interference from semantically similar objects and are prone to incorrect segmentation. We propose a spatial constraint network to overcome these problems. In particular, we introduce a time-varying sensor and a dynamic feature memory to adaptively store pixel information to facilitate the modeling of the target object, which greatly reduces information redundancy in the memory without missing critical information. Furthermore, we propose an efficient memory reader that is less computationally intensive and has a smaller footprint. More importantly, we introduce a spatial constraint module to learn spatial consistency to obtain more precise segmentation;the target and distractors can be identified by the learned spatial response. The experimental results indicate that our method is competitive with state-of-the-art methods on several benchmark datasets. Our method also achieves an approximately 30 FPS inference speed, which is close to the requirement for real-time systems. © 2023, The Authors. All rights reserved.

关键词： Real time systems

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Resilient Mechanism Against Byzantine Failure for Distributed Deep Reinforcement Learning

Resilient Mechanism Against Byzantine Failure for Distribute...

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International Symposium on software Reliability engineering (ISSRE)

作者： Mingyue Zhang Zhi Jin Jian Hou Renwei Luo Schhool of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies (PKU) MoE China School of Information Science & Technology Zhejiang Sci-Tech University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Distributed deep reinforcement learning(DDRL) has been used in distributed systems to better improve the adaptability. However, DDRL-based systems are also inevitably under the threat of Byzantine workers. There is an urgent need to enhance the resilience of the DDRL-based system against Byzantine failures. This paper proposes a resilient mechanism for mitigating the influence of Byzantine workers on DDRL-based systems. First, we formalize the DDRL-based system as a multi-armed bandit model for well capturing the collective effect of workers on the whole learning process, and then transforming the resilient mechanism design problem into the sampling policy optimization problem. Second, we propose a self-adaptation process for filtering out the harmful data generated by Byzantine workers and theoretically give a mathematical analysis of the understanding, demonstrating its effectiveness under ideal conditions. Third, based on a typical DDRL-based system (i.e., Asynchronous Advantage Actor-Critic, A3C), we implement a resilient distributed A3C (ReD-A3C). With extensive experiments on the DDRL benchmark tasks, we show that ReD-A3C outperforms available Byzantine tolerant approaches.

关键词： Deep learning Filtering Mathematical analysis Reinforcement learning Benchmark testing Robustness software reliability

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GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Gene Mutation

GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Ge...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Runfeng Lu Yuzhu Sun Haofeng Sun Xiao Fu Bin Luo Xiaojiang Du Jin Shi Nadjib Aitsaadi Mohsen Guizani State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA School of Information Management Nanjing University Nanjing China DAVID UVSQ Université Paris-Saclay France Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security.

关键词： Operating systems Image edge detection Genetic mutations Text categorization Fuzzing Libraries Robustness Security Smart phones Testing

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MrTF: Model Refinery for Transductive Federated Learning

arXiv

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arXiv 2023年

作者： Li, Xin-Chun Yang, Yang Zhan, De-Chuan State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China School of Artificial Intelligence Nanjing University Jiangsu Nanjing210023 China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China Department of Computing The Hong Kong Polytechnic University 100872 Hong Kong

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning (FL) paradigms are devoted to solving the data heterogeneity problem without considering the to-be-inferred data. We propose a novel learning paradigm named transductive federated learning (TFL) to simultaneously consider the structural information of the to-be-inferred data. On the one hand, the server could use the pre-available test samples to refine the aggregated models for robust model fusion, which tackles the data heterogeneity problem in FL. On the other hand, the refinery process incorporates test samples into training and could generate better predictions in a transductive manner. We propose several techniques including stabilized teachers, rectified distillation, and clustered label refinery to facilitate the model refinery process. Abundant experimental studies verify the superiorities of the proposed Model refinery framework for Transductive Federated learning (MrTF). The source code is available at https://***/lxcnju/MrTF. © 2023, CC BY-NC-ND.

关键词： Distillation

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Fundamentals of Delay-Doppler Communications: Practical Implementation and Extensions to OTFS

arXiv

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arXiv 2024年

作者： Li, Shuangyang Jung, Peter Yuan, Weijie Wei, Zhiqiang Yuan, Jinhong Bai, Baoming Caire, Giuseppe Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia State Key Lab. of ISN Xidian University Xi'An710071 China

The recently proposed orthogonal time frequency space (OTFS) modulation, which is a typical Delay-Doppler (DD) communication scheme, has attracted significant attention thanks to its appealing performance over doublyselective channels. In this paper, we present the fundamentals of general DD communications from the viewpoint of the Zak transform. We start our study by constructing DD domain basis functions aligning with the time-frequency (TF)- consistency condition, which are globally quasi-periodic and locally twisted-shifted. We unveil that these features are translated to unique signal structures in both time and frequency, which are beneficial for communication purposes. Then, we focus on the practical implementations of DD Nyquist communications, where we show that rectangular windows achieve perfect DD orthogonality, while truncated periodic signals can obtain sufficient DD orthogonality. Particularly, smoothed rectangular window with excess bandwidth can result in a slightly worse orthogonality but better pulse localization in the DD domain. Furthermore, we present a practical pulse shaping framework for general DD communications and derive the corresponding input-output relation under various shaping pulses. Our numerical results agree with our derivations and also demonstrate advantages of DD communications over conventional orthogonal frequency-division multiplexing (OFDM). © 2024, CC BY-NC-SA.

关键词： Pulse shaping

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