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arXiv 2022年

作者： Hu, Shengshan Zhang, Junwei Liu, Wei Hou, Junhui Li, Minghui Zhang, Leo Yu Jin, Hai Sun, Lichao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China Cluster and Grid Computing Lab China National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Services Computing Technology and System Lab China City University of Hong Kong Hong Kong Deakin University Australia Lehigh University United States

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modification for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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High Performance Solar-Blind Photodetectors Based on Plasma-Enhanced Atomic Layer Deposition of Thin Ga2o3 Films Annealed Under Different Atmosphere

SSRN

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SSRN 2022年

作者： Chen, Yu-Chang Chen, Ding-Bo Zeng, Guang Li, Xiao-Xi Li, Yu-Chun Zhao, Xue-Feng Chen, Na Wang, Ting-Yun Zhang, David Wei Lu, Hong-Liang State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Yiwu City322000 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute Communication and Data Science Shanghai University Shanghai200444 China

Solar-blind photodetectors (SBPDs) have attracted great interests for the potential applications as missile guidance, biological detection, and ultra-violet imaging. Despite the large number of Ga 2 O 3 -SBPDs reported, most of them with thick light absorption layers fail to achieve both high responsivity and fast response time simultaneously. In this work, high-performance SBPDs with 20 nm thick Ga 2 O 3 film were demonstrated with an extremely low dark current at 0.76 pA, a great photo-dark current ratio of 10 6 , a high responsivity of 6.18 A/W, and an ultra-fast decay time ~45 ms. Moreover, the films annealed in nitrogen exhibit a more pronounced amorphous-to-polycrystalline transition, while the samples after oxygen annealing show a lower recrystallization and less oxygen vacancy concentration. This finding opens up the pathways and provide an alternative strategy towards developing the high photoresponse performance required for future applications using solar-blind detection. © 2022, The Authors. All rights reserved.

关键词： Atomic layer deposition

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Simulate Human Saccadic Scan-Paths in Target Searching

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International Journal of Intelligence Science 2016年第1期6卷 1-9页

作者： Lijuan Duan Jun Miao David M. W. Powers Jili Gu Laiyun Qing Beijing Key Laboratory of Trusted Computing Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data College of Computer Science and Technology Beijing University of Technology Beijing China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing Technology CAS Beijing China School of Computer Science Engineering & Maths Flinders University of South Australia Adelaide South Australia Beijing Samsung Telecom R&D Center Beijing China University of Chinese Academy of Sciences Beijing China

Human saccade is a dynamic process of information pursuit. There are many methods using either global context or local context cues to model human saccadic scan-paths. In contrast to them, this paper introduces a model for gaze movement control using both global and local cues. To test the performance of this model, an experiment is done to collect human eye movement data by using an SMI iVIEW X Hi-Speed eye tracker with a sampling rate of 1250 Hz. The experiment used a two-by-four mixed design with the location of the targets and the four initial positions. We compare the saccadic scan-paths generated by the proposed model against human eye movement data on a face benchmark dataset. Experimental results demonstrate that the simulated scan-paths by the proposed model are similar to human saccades in term of the fixation order, Hausdorff distance, and prediction accuracy for both static fixation locations and dynamic scan-paths.

关键词： Saccadic Scan-Paths Eye Movement Fixation Locations Dynamic Scan-Paths

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Integrated Multi-Omics Analysis Identities a Machine Learning-Derived Signature for Predicting Prognosis and Therapeutic Vulnerability in Clear Cell Renal Cell Carcinoma

SSRN

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SSRN 2024年

作者： Chi, Shengqiang Ma, Jing Ding, Yiming Lu, Zeyi Zhou, Zhenwei Wang, Mingchao Li, Gonghui Chen, Yuanlei Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou311121 China Department of Urology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou310016 China The Engineering Research Center of EMR and Intelligent Expert System Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China

Background: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. Methods: To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data. Afterwards, we applied bootstrapping in univariate Cox regression and the Boruta algorithm to pinpoint clinically relevant genes. Based on this, we developed a robust Prognosis and therapeutic vulnerability signature (PTVS) using seven machine learning algorithms, capable of predicting patient prognosis and drug ***: Our analysis revealed two distinct ccRCC subtypes with differential prognostic implications, notably identifying subtype 2 with poorer outcomes. Patients in the low PTVS group exhibited superior prognosis statistics and an augmented sensitivity to immunotherapy, features consistent with a 'hot tumor' phenotype. Conversely, individuals within the high PTVS group exhibited diminished prognosis statistic and restricted advantages from immunotherapy. Importantly, the PTVS holds future potential as a notable biomarker for guiding personalized treatment strategies, with four prospective targets (CTSK, XDH, PKMYT1, and EGLN2) indicating therapeutic promise in patients scoring high on PTVS. Conclusions: The integrative analysis of multi-omics data profoundly enhances the molecular stratification of ccRCC, underscoring far-reaching impact of such comprehensive profiling on its therapeutic strategies. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Partial Deep Domain Adaptation for Non-Intrusive Load Monitoring

Partial Deep Domain Adaptation for Non-Intrusive Load Monito...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yuying Li Yang Yang Chen Wang Runqin Liu Hongbo Jiang Wenping Liu Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China China Unicom Central South Research Institute Changsha China Hunan University Changsha China Hubei University of Economics Wuhan China

With the widespread deployment of smart meters and the burgeoning integration of the Ubiquitous Power Internet of Things (UPIoT), Non-Intrusive Load Monitoring (NILM) has emerged as an essential technology, providing granular insights into device-level energy consumption. However, NILM model generalization is impeded by user and appliance heterogeneity. Domain adaptation has been leveraged to enhance NILM models, but it typically necessitates consistent appliance categorization across households. Addressing this, our study presents Weighted Adaptation NILM (WeAd-NILM), a partial deep domain adaptation model designed for Partial Domain Adaptation (PDA) scenarios that accommodate diverse appliance categories, aligning with real-world load decomposition tasks. WeAd-NILM introduces a sample weighting mechanism tailored for PDA, mitigating negative transfer from outliers. Through distribution analysis and multi-objective regression, WeAd-NILM effectively performs load decomposition, fostering knowledge transfer. Verified on UK-DALE and REFIT datasets, WeAd-NILM demonstrates exceptional transferability and load decomposition accuracy.

关键词：

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Sledge: Towards Efficient Live Migration of Docker Containers

Sledge: Towards Efficient Live Migration of Docker Container...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

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Uplink Precoding Design for Cell-Free Massive MIMO With Iteratively Weighted MMSE

Uplink Precoding Design for Cell-Free Massive MIMO With Iter...

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作者： Wang, Zhe Zhang, Jiayi Ngo, Hien Quoc Ai, Bo Debbah, Merouane Frontiers Science Center for Smart High-Speed Railway System The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Institute of Electronics Communications and Information Technology Queen’s University Belfast BelfastBT3 9DT United Kingdom State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis The Frontiers Science Center for Smart High-Speed Railway System Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Research Center of Networks and Communications Shenzhen518066 China Technology Innovation Institute Abu Dhabi United Arab Emirates CentraleSupélec University Paris-Saclay Gif-sur-Yvette91192 France

In this paper, we investigate a cell-free massive multiple-input multiple-output system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) for the fully centralized processing scheme and large-scale fading decoding (LSFD) scheme. To further improve the SE performance, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on Iteratively Weighted sum-Minimum Mean Square Error (I-WMMSE) algorithms, which rely on the iterative minimization of weighted Mean Square Error (MSE), are proposed for two processing schemes investigated. Furthermore, with maximum ratio combining applied in the LSFD scheme, we derive novel closed-form achievable SE expressions and optimal precoding schemes. Numerical results validate the proposed results and show that the I-WMMSE precoding schemes can achieve excellent sum SE performance with a large number of UE antennas. © 1972-2012 IEEE.

关键词： MIMO systems

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Factors correlation mining on railway accidents using association rule learning algorithm

Factors correlation mining on railway accidents using associ...

引用

International Conference on intelligent Transportation

作者： Yakun Wang Wei Zheng Hairong Dong Pengfei Gao National Research Center of Railway Safety Assessment Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Although much research work for the operation safety has been taken in the railway domain, some accidents still occur because past experiences of accident analysis were not fully accumulated for safety improvement. This study aims to identify potential causal relationships among the many factors playing a role in railway accidents. A new interestingness measure, Confidence_interestingness ("C_" Inter) and corresponding improved algorithm, Positive and Negative Association Rules Algorithm based on "C_" Inter (PNARA_CI) were put forward in our study. Compared with traditional association rule mining algorithms, the PNARA_CI does not generate candidate association rules by means of frequent itemsets, but by the combination between every two accident factors, which can mine the positive and negative association rules with practical value to the maximum. And they were applied to railway accidents data to explore the association rules of the causal factors in the case study. The effectiveness of the algorithm was verified.

关键词： Railway accidents data mining Rail transportation Itemsets Safety databases Task analysis

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A Dynamic Fault Tree Based CBTC Onboard ATP system Safety Analysis Method*

A Dynamic Fault Tree Based CBTC Onboard ATP System Safety An...

引用

International Conference on intelligent Transportation

作者： Pengfei Gao Chao Liu Hairong Dong Wei Zheng National Research Centre of Railway Safety Assessment and the Beijing Key Laboratory of Intelligent Traffic Data Security and Privacy Protection Technology Beijing Jiaotong University Beijing China National Research Centre of Railway Safety Assessment Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

To obtain refined safety requirements during system architecture design stage, the traditional static fault tree analysis mothed is widely used to analyse the logical relationship between basic hazardous events leading to system hazards in railway signal system, and to identify the weak and key equipment of the system. In order to accurately characterize the dynamic behaviours of the system and improve the accuracy and credibility of analysis results, a dynamic fault tree analysis mothed of railway signal system based on failure propagation modelling was proposed. According to failure logic modelling of dynamic failure behaviours, conforming to signal system function design, component failure model was established within SimFIA platform, and fault trees of system hazards were attained by model simulation. The result of Communication-Based Train Control (CBTC) system on-board equipment case study shows that the fault tree of complex system function are generated by the dynamic fault tree analysis mothed based on Failure Propagation and Transformation Notation (FPTN) modelling, which guaranteed the safety analysis outcomes was accurate and credible.

关键词： Safety Analytical models system analysis and design Fault trees Rail transportation Discrete Fourier transforms systems architecture

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