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A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

Journal of computer science & technology 2020年第1期35卷 209-220页

作者： Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518055China Alibaba Inc. Hangzhou 311121China Department of Computer Science Wayne State UniversityMichiganMI 48202U.S.A

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be *** resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable *** of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource *** this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC *** is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural *** experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.

关键词： resource management neural network resource efficiency tail latency

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A Deep Neural Network for Detecting Broken and Scattered Power Lines in Complex Aerial Images

A Deep Neural Network for Detecting Broken and Scattered Pow...

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International Conference on Artificial Intelligence and computer Information technology (AICIT)

作者： Qiugen Pei Yuxuan Lai Nianxing Wei Shiting Wu Enterprise Architecture and Digitalization Department Guangdong Power Grid Co. Ltd. Guangzhou China Dept. School of Computer Science Guangdong University of Technology Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the “EWF _detection” (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow

关键词： Adaptation models Image recognition Shape Lighting Object detection Feature extraction Power industry Robustness Maintenance Wire

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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HealthyBlockchain for Global Patients

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computers, Materials & Continua 2021年第8期68卷 2431-2449页

作者： Shada A.Alsalamah Hessah A.Alsalamah Thamer Nouh Sara A.Alsalamah College of Computer and Information Sciences King Saud UniversityRiyadh11451Saudi Arabia Digital Health and Innovation Science DivisionWorld Health OrganizationGenevaCH-1211Switzerland MIT Department of Mechanical Engineering Massachusetts Institute of Technology(MIT)Cambridge02139MAUSA College of Engineering and Architecture Al Yamamah UniversityRiyadh11451Saudi Arabia Trauma and Acute Care Surgery Unit College of MedicineKing Saud UniversityRiyadh11451Saudi Arabia College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia

An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making *** healthcare information systems(HISs)fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care ***,in recent times,the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to *** blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in *** this paper,we aim to study the use of blockchain in equipping struggling HISs to cope with the demands of the new healthcare delivery model,by proposing HealthyBlockchain as a granular patient-centered ledger that digitally tracks a patient’s medical transactions all along the treatment pathway to support the care *** patient-centered ledger is a neutral tamper-proof trail timestamp block sequence that governs distributed patient information across the decentralized discrete *** connects patients,clinicians,and healthcare providers to facilitate a transparent,trustworthy,and secure supporting platform.

关键词： Blockchain eHealth electronic health record global patient healthcare information system information security legacy system patient-centered care privacy smart contract trust

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IEEE 8th VR Workshop on Sonic Interactions for Virtual Environments

IEEE 8th VR Workshop on Sonic Interactions for Virtual Envir...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Stefania Serafin Ali Adjorlu Rolf Nordahl Federico Fontana Michele Geronazzo Tifanie Bouchara Florent Berthaut Romain Michon Dorte Hammershøj Lorenzo Picinali Department of Architecture Design and Media Technology Aalborg University Copenhagen Denmark Department of Mathematics Computer Science and Physics University of Udine Udine Italy Department of Engineering and Management University of Padova Vicenza Italy LISN CNRS Université Paris-Saclay France CRIStAL CNRS Univ. Lille Lille France INRIA Lyon France Department of Electronic Systems Aalborg University Denmark Dyson School of Design Engineering Imperial College London London UK

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Sonic interaction design is defined as the study and exploitation of sound as one of the principal channels conveying information, meaning, and aesthetic/emotional qualities in interactive contexts. This field lies at the intersection of interaction design, and sound and music *** the virtual reality community, the focus of research on topics related to auditory feedback has been rather limited when compared, for example, to the focus placed on visual feedback or even on haptic feedback. However, in such communities as film production or product sound design it is well known that sound is a powerful way to communicate meaning and emotion to a scene or a *** 2025 is the 8th of a series of workshops, whose main goal is to increase awareness among the virtual reality community of the importance of sonic elements when designing virtual/augmented/mixed reality (XR hereafter) environments. Participants to the workshop will also discuss how research in other related fields such as film sound theory, product sound design, sound and music computing, game sound design, and accessibility can inform designers of XR environments. Moreover, the workshop will feature state-of-the-art research in the field of sound for XR environments.

关键词： Visualization Three-dimensional displays Conferences Spatial audio Music Virtual environments Production Games User interfaces Haptic interfaces

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Cuddapah Anitha Dalia Al-Tarazi GD. Vignesh A. Vinotha Vasuki Jayant Giri M. Dinesh Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of Architecture Engineering Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.

关键词： Vehicular ad hoc networks Routing Throughput Synchronization Resource management Dedicated short range communication Particle swarm optimization Artificial intelligence Vehicle dynamics Testing

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The artificial neural networks (Ann) for batik detection based on textural features 7

The artificial neural networks (Ann) for batik detection bas...

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7th Mathematics, science, and computer science Education International Seminar, MSCEIS 2019

作者： Kasim, Anita Ahmad Bakri, Muhammad Septiarini, Anindita Departemen of Information Technology Tadulako University Palu Indonesia Department of Architecture Tadulako University Palu Indonesia Department of Computer Science Mulawarman University Samarinda Indonesia

ISBN: (纸本)9781631902567

This study aims to utilize artificial neural networks to distinguish batik motifs and non-batik fabric motifs. Several important steps are needed, namely the process of acquiring batik and non-batik images, pre-transforming batik and non-batik images to gray scale forms, texture feature extraction in gray scale images and detection of motifs using networks artificial nerve. Image acquisition is done by collecting batik and not batik images from several different motifs. Processing data sets is divided into 70% as training data and 30% as testing data. Artificial neural network models used in this research use the Backpropagation learning algorithm by comparing the Scaled conjugate gradient algorithm (trainscg) training method and the Levenberg-Marquardt algorithm (trainlm) training method. The results obtained for the accuracy using the Scaled conjugate gradient algorithm (trainscg) training method were higher with an accuracy value of 84.12%, compared to the Levenberg-Marquardt algorithm (trainlm) method by 86.11%. © 2020 EAI.

关键词： Neural networks

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A Quantum Generative Adversarial Network-based Intrusion Detection System

A Quantum Generative Adversarial Network-based Intrusion Det...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Md Abdur Rahman Hossain Shahriar Victor Clincy Md Faruque Hossain Muhammad Rahman Department of Mathematics Jahangirnagar University Savar Dhaka Bangladesh Department of Information Technology Kennesaw State University Georgia United States Department of Computer Science Kennesaw State University Georgia United States College of Architecture and Construction Management Kennesaw State University Georgia United States College of Information and Mathematical Sciences Clayton State University Georgia United States

Machine learning has become widely accepted because of its diverse approaches to deal with a variety of cyber security issues. However, their capricious nature of security threats makes classical machine learning cyber systems vulnerable. Moreover, more samples in a big data dataset in classical machine learning approaches could produce the security defence systems weaken. It may create accurate outcomes by processing information which takes longer than expected, or observe poor accuracy because of inefficient training as well as other issues. However, quantum systems have the potential to produce atypical patterns which can not be possible to produce efficiently by classical systems, so we can postulate that quantum computers could use these advantages in that it could outperform the capabilities of classical computers on machine learning tasks. To be specific, an intrusion detection system can detect attack packets or sequence of attack packets at TCP/IP or other protocol level data based on certain patterns present or by profiling to detect anomalies. O(poly(n) gates are required to enable the use of potentially advantageous quantum algorithms with quantum states using he Quantum generative adversarial networks (qGAN) implemented by Qiskit which is a quantum computing tool of IBM.

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Robotic additive construction of bar structures: unified sequence and motion planning

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Construction Robotics 2021年第2期5卷 115-130页

作者： Huang, Yijiang Garrett, Caelan R. Ting, Ian Parascho, Stefana Mueller, Caitlin T. Building Technology Program Department of Architecture Massachusetts Institute of Technology Cambridge USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge USA CREATE Laboratory School of Architecture Princeton University Princeton USA

Additive robotic construction of building-scale discrete bar structures, such as trusses and space frames, is increasingly attractive due to the potential improvements in efficiency, safety, and design possibilities. However, programming complex robots, such as manipulators with seven degrees of freedom, to successfully complete construction tasks can be tedious, challenging, or impossible for a human to do manually. Namely, the structure must be constructed in a sequence that preserves structural properties, such as stiffness, at each step. At the same time, this sequence must allow for the robot to precisely manipulate elements within the in-progress structure while respecting geometric constraints that, for example, ensure the robot does not collide with what it has built. In this work, we present an automated and newly generalized planning approach for jointly finding a construction sequence and robot motion plan for additive construction that satisfies these requirements. Our approach can be applied in a variety of additive construction processes, and we demonstrate it specifically on spatial extrusion and discrete bar assembly in this paper. We demonstrate the effectiveness of our approach on several simulated and real-world extrusion and assembly tasks, including a human-scale physical prototype, for which our algorithm is deployed for the first time to plan the assembly of a complicated double tangent bar system design.

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A Shared Pose Regression Network for Pose Estimation of Objects from RGB Images

A Shared Pose Regression Network for Pose Estimation of Obje...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Stefan Hein Bengtson Hampus Åström Thomas B. Moeslund Elin A. Topp Volker Krueger Department of Architecture Design and Media Technology Visual Analysis and Perception (VAP) Laboratory Pioneer Centre for AI Denmark Aalborg University Aalborg Denmark Department of Computer Science Lund University Robotics and Semantic Systems Lund Sweden

In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance.

关键词： Training Pose estimation Memory management Pipelines Feature extraction Rendering (computer graphics) Real-time systems

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