检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Liu, Yunfei Ma, Xingjun Bailey, James Lu, Feng State Key Laboratory of Virtual Reality Technology and Systems School of CSE Beihang University Beijing China Peng Cheng Laboratory Shenzhen China School of Information Technology Deakin University Geelong Australia School of Computing and Information Systems University of Melbourne Australia

Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at training time. A backdoor attack installs a backdoor into the victim model by injecting a backdoor pattern into a small proportion of the training data. At test time, the victim model behaves normally on clean test data, yet consistently predicts a specific (likely incorrect) target class whenever the backdoor pattern is present in a test example. While existing backdoor attacks are effective, they are not stealthy. The modifications made on training data or labels are often suspicious and can be easily detected by simple data filtering or human inspection. In this paper, we present a new type of backdoor attack inspired by an important natural phenomenon: reflection. Using mathematical modeling of physical reflection models, we propose reflection backdoor (Refool) to plant reflections as backdoor into a victim model. We demonstrate on 3 computer vision tasks and 5 datasets that, Refool can attack state-of-the-art DNNs with high success rate, and is resistant to state-of-the-art backdoor defenses. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

引用

arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning-based Methods for Resource Scheduling in cloud computing: A Review and Future Directions

arXiv

引用

arXiv 2021年

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar Xue, Ruini Song, Liang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Laboratory Department of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

With the acceleration of the Internet in Web 2.0, cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging cloud computing. Considering the growing complexity of cloud computing, future cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning (DL) and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based cloud scheduling. © 2021, CC BY.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Triple-Band Broadband Antenna for the Internet of Vehicles

引用

IEEE Transactions on Vehicular Technology 2025年

作者： An, Wenxing Zhang, Xiaochi Zhao, Wenli Luo, Yu Li, Min Ma, Kaixue Tianjin University Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology School of Microelectronics Tianjin300072 China Qingdao Institute of Ocean Engineering of Tianjin University Qingdao Key Laboratory of Marine Information Perception and Transmission Tianjin Engineering Center of Integrated Circuit and Computing Systems Tianjin300072 China

A single-port triple-band antenna is presented for the Internet of Vehicles. This vehicle-mounted antenna can cover the frequency bands of civil wireless base station networks, including the low-frequency band from 0.698 to 0.96 GHz, the mid-frequency band from 1.71 to 2.69 GHz, and the high-frequency band from 3.3 to 4.99 GHz. The highest frequency is seven times higher than the lowest frequency. The proposed antenna design incorporates multi-resonant structures, including the loop, stair-shaped dipole, and director for triple-band performance. The measurement results show that the -10-dB bandwidths are 0.67∼0.97 GHz, 1.67∼2.74 GHz, and 3.16∼5.11 GHz with relative bandwidths of 36.6%, 48.5%, and 47.1%. Broadside radiation patterns have been achieved with average gains of 5.74 dBi, 8.76 dBi, and 11.16 dBi for the target bands. The proposed design can offer triple-band and broadband performance with a compact structure, significantly reducing mass, volume, and cost for multi-band vehicular communication systems. © 1967-2012 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Unity makes strength: Coalition Formation-based Group-buying for Timely UAV Data Collection

Unity makes strength: Coalition Formation-based Group-buying...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nan Qi Yeting Huang Wen Sun Shi Jin Theodoros A. Tsiftsis Qihui Wu Xiang Su National Mobile Communications Research Laboratory Southeast University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Intelligent Systems Science & Engineering Jinan University Zhuhai China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland

ISBN: (纸本)9781665435413

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

关键词： Wireless sensor networks Games Data collection Autonomous aerial vehicles Search problems information age Trajectory

来源：评论

学校读者我要写书评

暂无评论

Compliance Control of Deburring Robots based on Force Impedance

Compliance Control of Deburring Robots based on Force Impeda...

引用

Chinese Automation Congress (CAC)

作者： Guanhua Xu Zhipeng Wang Jiafu Zhang Bo Yang Zhengtuo Wang Yuetong Xu The State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Tongji University Shanghai China School of Computing and Information Systems the University of Melbourne Melbourne Australia

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

To achieve active compliance in the robot deburring process, a compliance control method of deburring robots based on force impedance is proposed. An impedance model between the reference deburring trajectory and contact force is constructed, based on the force feedback. The force impedance is integrated into the position control loop of robots in the Cartesian coordinates. On-line adjustment of the robot displacement and velocity is realized using normal force and tangential force, respectively. The control system simulation is carried out in the Simulink environment using the S-function. Results show that this method guarantees the displacement tracking performance of the robot, and achieves the displacement compliance through the normal force, as well as the velocity compliance through the tangential force.

关键词： Robots Robot kinematics Deburring Force Impedance Robot sensing systems Position control

来源：评论

学校读者我要写书评

暂无评论

Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

SSRN

引用

SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Reliable shot identification for complex event detection via visual-semantic embedding

arXiv

引用

arXiv 2021年

作者： Luo, Minnan Chang, Xiaojun Gong, Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China School of Computing Technologies RMIT University MelbourneVIC3000 Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Nanjing University of Science and Technologyy Nanjing210094 China

Multimedia event detection is the task of detecting a specific event of interest in an user-generated video on websites. The most fundamental challenge facing this task lies in the enormously varying quality of the video as well as the high-level semantic abstraction of event inherently. In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance. Instead of treating the instances equally, we associate each instance with a reliability variable to indicate its importance and then select reliable instances for training. To measure the reliability of the varying instances precisely, we propose a visual-semantic guided loss by exploiting low-level feature from visual information together with instance-event similarity based high-level semantic feature. Motivated by curriculum learning, we introduce a negative elastic-net regularization term to start training the classifier with instances of high reliability and gradually taking the instances with relatively low reliability into consideration. An alternative optimization algorithm is developed to solve the proposed challenging non-convex non-smooth problem. Experimental results on standard datasets, i.e., TRECVID MEDTest 2013 and TRECVID MEDTest 2014, demonstrate the effectiveness and superiority of the proposed method to the baseline algorithms. © 2021, CC BY.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

Quality Optimization of Live Streaming Services over HTTP with Reinforcement Learning

Quality Optimization of Live Streaming Services over HTTP wi...

引用

2021 IEEE Global Communications Conference (GLOBECOM)

作者： F. Tashtarian R. Falanji A. Bentaleb A. Erfanian P. S. Mashhadi C. Timmerer H. Hellwagner R. Zimmermann Christian Doppler Laboratory ATHENA Institute of Information Technology Alpen-Adria-Universität Klagenfurt Austria Sharif University of Technology Tehran Iran School of Computing National University of Singapore (NUS) Center for Applied Intelligent Systems Research (CAISR) Halmstad University Sweden

ISBN: (纸本)9781728181059

Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to $3.7\times$ .

关键词： Scalability Conferences Reinforcement learning Streaming media Internet Quality of experience Servers

来源：评论

学校读者我要写书评

暂无评论

Adaptive Stepwise Feature Selection Approach for EEG-Based Epileptic Seizure Classification

Adaptive Stepwise Feature Selection Approach for EEG-Based E...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sunday Timothy Aboyeji Wenfang Zhou Yuan Tao Mingxing Zhu Oluwarotimi Williams Samuel Ijaz Ahmad Guoru Zhao Ji Yi Michael Chi Fai Tong Xin Wang Yi Guo Shixiong Chen CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Otolaryngology Luohu District People's Hospital Shenzhen China Department of Otorhinolaryngology Peking University Shenzhen Hospital Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen Shenzhen China School of Computing and Engineering University of Derby Derby UK Department of Ophthalmology School of Medicine Johns Hopkins University Baltimore USA Department of Otorhinolaryngology Head and Neck Surgery The Chinese University of Hong Kong Hong Kong China Department of Neurology Shenzhen People’s Hospital Shenzhen China School of Medicine The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent advancements in feature selection (FS) optimization algorithms have influenced the field of epileptic seizure classification. However, integrating these optimization algorithms into machine learning (ML) models often creates time complexity, limiting their clinical deployment. To address this issue, we propose an innovative adaptive stepwise FS method tailored for epileptic seizure detection (ESD). First, a discrete wavelet transform (DWT) was applied to the preprocessed signal to get three levels of the db4 wavelet family within the frequency range pertinent to epileptic seizure classification. Linear and nonlinear features are then extracted from each level of the DWT. The selected features are initially ranked using the minimum relevance, maximum redundancy (mRMR) FS technique. After that, a stepwise FS approach was applied to the ranked features to optimize the performance of Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) classifiers. The experiment was performed on a publicly accessible CHB-MIT dataset in a patient-independent approach. The model's performance was assessed using accuracy, sensitivity, and specificity. The results show an improved performance of the ML models with the integration of stepwise algorithm into the mRMR technique. Among the classifiers, RF exhibited superior performance with accuracy, sensitivity, and specificity of 87.69%, 91.53%, and 83.86%, respectively, when 12 features were selected. Our proposed stepwise feature selection method (PSFS) performs similarly to generalize forward feature selection (GFFS), with an average accuracy of 88.37% and 88.57%, respectively across selected features with less computation. This makes PSFS a very efficient and effective FS in epileptic seizure classification.

关键词： Support vector machines Radio frequency Accuracy Redundancy Sensitivity and specificity Feature extraction Brain modeling Classification algorithms Discrete wavelet transforms Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：