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IEEE international conference on Real-time computing and Robotics (IEEE-RCAR)

作者： Peng, Yuanzhe Ou, Yongsheng Feng, Wei Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen 518055 Peoples R China Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sy Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728172934

Since it is difficult to work out a complete and accurate analytical model for some complex nonlinear dynamical systems, we mainly propose a method for learning stable control with fast adaptive neural network. Furthermore, we provide a rigorous analytical evaluation of our proposed method in terms of stability. In order to validate the theoretical analysis, our proposed method has been applied in the stable control of a wheeled inverted pendulum (WIP) platform. It achieves high prediction accuracy and fast learning speed. In addition, the proposed method has incremental learning capabilities. When a new instance is provided, there is no need to retrain the entire training set, thus, the controller is more effective and more generalized.

关键词： Adaptive Control learning Ability Dynamical System Wheeled Inverted Pendulum (WIP)

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A Mechanism-Data Driven Fault Prediction Method Based on Cloud-Terminal Fusion 5

A Mechanism-Data Driven Fault Prediction Method Based on Clo...

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5th IEEE international conference on Cloud computing and Big Data Analytics, ICCCBDA 2020

作者： Jianfeng, Lu Jian, Wei Zijie, Zhao Juecheng, Hu CIMS Research Center of Tongji University No. 4800 Caoan Highway Shanghai China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160245

The feed axis is a key component of machine tools. Once the feed axis fails, it will have a huge impact on the overall performance of machine tool. The traditional fault prediction methods of feed axis mostly judge according to the operating mechanism, but due to the increasing complexity of components, it has become more and more difficult to establish the operational mechanism model. With the application of machine learning methods in the field of fault prediction, the data-driven method has made great progress, but the accuracy of prediction is limited. Based on the two reasons above, this paper proposes a mechanism-date driven fault prediction method of feed axis. The data-driven model requires a large amount of historical data for training, but the storage capacity and computing power of the terminal device are limited. Therefore, the cloud-terminal fusion architecture is used to implement the mentioned fault prediction model above. © 2020 IEEE.

关键词： learning systems

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Guest Editorial for Special Section on the 13th international conference on Intelligent computing (ICIC)

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第3期16卷 749-750页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, M. Michael Tongji Univ Machine Learning & Syst Biol Lab Shanghai 200072 Peoples R China Polytech Bari Elect & Elect Dept Via Orabona I-70126 Bari Italy Indian Inst Technol Dept Biotechnol Chennai 600036 Tamil Nadu India

The papers presented in this special section were presented at the Thirteenth international conference on Intelligent computing (ICIC) that was held in Liverpool, UK, on August 7-10, 2017. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Meetings Computational biology Bioinformatics machine learning Artificial intelligence

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Disorder detection of tomato plant(solanum lycopersicum) using IoT and machine learning 1

Disorder detection of tomato plant(solanum lycopersicum) usi...

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1st international conference on Emerging Electrical Energy, Electronics and computing Technologies 2019, ICE4CT 2019

作者： Khan, Saiqa Narvekar, Meera Computer Engineering Department M.H.SabooSiddik College of Engineering Mumbai India Computer Engineering Department D.J.Sanghvi College of Engineering Mumbai India

India is an agricultural country and this sector accounts for 18 percent of India's GDP. This sector is the backbone of the country and focuses on better yield by using pesticides and fertilizers to prevent plant disorders which directly affects the yield. The primary method adopted for detecting disorders is through visual observation and other methods are quite expensive. Many authors have proposed solutions to this problem such as IoT for grapes, or system designed for accurate disorder detection using machine learning with limited scope. This paper showcases a prototype that uses multi-modal analysis through sensor data, computer vision. The main objective of this system is to accurately detect disorders in tomato plant using IoT, machine learning, Cloud computing, and Image Processing. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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Intrusion Traffic Detection and Characterization using Deep Image learning 18

Intrusion Traffic Detection and Characterization using Deep ...

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18th IEEE international conference on Pervasive Intelligent and computing (PICom)

作者： Kaur, Gurdip Lashkari, Arash Habibi Rahali, Abir Univ New Brunswick Canadian Inst Cybersecur Fredericton NB Canada

ISBN: (数字)9781728166094

ISBN: (纸本)9781728166094

The security community has witnessed an unprecedented upsurge in cyber attacks in recent years. These attacks have proved to be successful in achieving their catastrophic objectives. Intrusion detection and prevention systems remain the principal point of defense against these devastating attacks. However, most of the anomaly datasets in the past are neither up-to-date nor reliable. Researchers used various machine learning techniques to classify anomaly-based attacks due to their capability to keep pace with the evolution of such attacks and gave encouraging predictions. Nevertheless, deep neural networks turned out to be revolutionary in detecting and characterizing such intrusions. In this paper, first of all, we propose an imagebased deep neural model to classify various attacks by using two comprehensive datasets called CICIDS2017 and CSE-CICIDS2018. Secondly, we provide a list of best network flow features to identify these attacks. We deploy a convolutional neural network model to classify and characterize different attacks with promising evaluation results.

关键词： intrusion deep learning convolutional neural networks network traffic anomaly detection traffic analysis

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SVQN: SEQUENTIAL VARIATIONAL soft Q-learning NETWORKS 8

SVQN: SEQUENTIAL VARIATIONAL SOFT Q-LEARNING NETWORKS

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8th international conference on learning Representations, ICLR 2020

作者： Huang, Shiyu Su, Hang Zhu, Jun Chen, Ting Dept. of Comp. Sci. & Tech. BNRist Center Institute for AI THBI Lab Tsinghua University China

Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions. Standard reinforcement learning algorithms for solving Markov Decision Processes (MDP) tasks are not applicable, as they cannot infer the unobserved states. In this paper, we propose a novel algorithm for POMDPs, named sequential variational soft Q-learning networks (SVQNs), which formalizes the inference of hidden states and maximum entropy reinforcement learning (MERL) under a unified graphical model and optimizes the two modules jointly. We further design a deep recurrent neural network to reduce the computational complexity of the algorithm. Experimental results show that SVQNs can utilize past information to help decision making for efficient inference, and outperforms other baselines on several challenging tasks. Our ablation study shows that SVQNs have the generalization ability over time and are robust to the disturbance of the observation. © 2020 8th international conference on learning Representations, ICLR 2020. All rights reserved.

关键词： Decision making

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Response to Co-resident Threats in Cloud computing Using machine learning 33rd

Response to Co-resident Threats in Cloud Computing Using Mac...

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33rd international conference on Advanced Information Networking and Applications, AINA-2019

作者： Lin, Chu-Hsing Lu, Hsiao-Wen Department of Computer Science Tunghai University Taichung40704 Taiwan

ISBN: (纸本)9783030150310

Virtualization technologies in cloud computing brings merits for resource utilization and on-demand sharing. However, users can face new security risks when they use the virtualized platforms. The co-resident attack means that the malicious users build side channels and threaten the virtual machines co-located on the same server. In 2017, Abazari et al. proposed a multi-objective, under the constraints of minimum cost and threat, response to co-resident attacks in cloud environment. In this paper, we aimed to propose a novel method for countermeasures decision to the attacks. We used machine learning to train the intrusion response model and conducted a set of experiments to demonstrate the effect of the proposed model. It showed that the near optimal solutions with good accuracy is obtainable and the response efficiency is improved. © 2020, Springer Nature Switzerland AG.

关键词： Cloud computing

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Network Intrusion Detection System with Stream machine learning in Fog Layer and Online Labeling in Cloud Layer

Network Intrusion Detection System with Stream Machine Learn...

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Electronic Communications, Internet of Things and Big Data (ICEIB), international conference on

作者： Shih-Hsien Chuang Ren-Chieh Yang Sheng-De Wang National Taiwan University Taipei Taiwan

ISBN: (纸本)9781665437561

We proposed a network intrusion detection system that combines a stream machine learning model in the fog layer and an online labeling model in the cloud layer. The stream learning model is based on the Adaptive XGBoost machine learning algorithm, aiming to detect anomaly network traffic. The online labeling model is a batch machine learning model based on the Random Forest algorithm and is responsible to label unknown traffic and provide updates to the stream learning model in the fog layer. The proposed solution effectively detects abnormal traffic in the fog layer that is connected with IoT devices. The stream learning model updates the model at a lower cost as compared to the batch learning approach. To evaluate the proposed system, contemporary datasets are used to test the accuracy of the models. The experiment results show that the proposed scheme effectively achieves good classification accuracy with the cloud layer providing updates to the fog layer. The result is about 17.6% and 9.0% better than the baseline method for the UNSW-NB15 dataset and CIC-IDS2017 dataset, respectively. In addition, the stream learning approach can provide higher throughput than the batch learning approach.

关键词： Measurement Adaptation models Cloud computing machine learning algorithms Costs Network intrusion detection Telecommunication traffic

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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3rd international conference on machine learning and soft computing, icmlsc 2019

ISBN: (纸本)9781450366120

The proceedings contain 48 papers. The topics discussed include: predicting absenteeism at work using tree-based learners;a study on machine learning for steganalysis;image-based silkworm egg classification and counting using counting neural network;classification of grain discoloration via transfer learning and convolutional neural networks;multi-view neural network integrating knowledge for patient self-diagnosis;a new mammography lesion classification method based on convolutional neural network;backbone solving algorithm based on heuristic thinking;solving dietary planning problem using particle swarm optimization with genetic operators;MMSIA: improved max-min scheduling algorithm for load balancing on cloud computing;and an improvement of fuzzy logic based clustering combined for mobile sink algorithm.

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Efficient Binary Static Code Data Flow Analysis Using Unsupervised learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： Data flow analysis

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