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Goal-Oriented Navigation with Avoiding Obstacle based on Deep Reinforcement learning in Continuous Action Space 21

Goal-Oriented Navigation with Avoiding Obstacle based on Dee...

引用

21st international conference on Control, Automation and Systems (ICCAS)

作者： Hien, Pham Xuan Kim, Gon-Woo Chungbuk Natl Univ Dept Control & Robot Engn Cheongju South Korea Chungbuk Natl Univ Dept Intelligent Syst & Robot Cheongju South Korea

ISBN: (纸本)9788993215212

Obstacle avoidance problems using Deep Reinforcement learning (DRL) are becoming possible solutions for autonomous mobile robots. In real-world situations with stationary and moving obstacles, mobile robots must be able to navigate to a goal and safely avoid collisions. This work is an extension of ongoing research on the navigation approach for a mobile robot. We show that through the proposed DRL, a goal-oriented collision avoidance model can be trained end-to-end without manual turning or supervision by a human operator. We suggest performing the obstacle avoidance algorithm of the mobile robot in both simulated environments and continuous action space of the real world. Finally, we measure and evaluate the obstacle avoidance capability through data collection of hit ratio metrics during robot execution.

关键词： deep reinforcement learning Q-learning obstacle avoidance robot sensing systems path planning

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data Poisoning Attack to X-armed Bandits 21

Data Poisoning Attack to X-armed Bandits

引用

21st IEEE international conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022

作者： Luo, Zhi Li, Youqi Chen, Lixing Xu, Zichuan Zhou, Pan Huazhong University of Science and Technology School of Cyber Science and Engineering China Beijing Institute of Technology School of Cyberspace Science and Technology School of Computer Science China Shanghai Jiao Tong University Institute of Cyber Science and Technology China Dalian University of Technology School of Software China Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China

ISBN: (纸本)9781665494250

X-armed bandits have achieved the state-of-the-art performance in optimizing unknown stochastic continuous functions, which can model many machine learning tasks, specially in big data-driven personalized recommendation. However, bandit algorithms are vulnerable to adversarial attacks. Existing works mainly focus on attacking multi-armed bandits in discrete setting;nevertheless, the attacks against X-armed bandits in continuous setting have not been well explored. In this paper, we aim to bridge this gap and investigate the robustness problem for the X-armed bandits. Specifically, we consider data poisoning attack and propose an attack algorithm named Confidence Poisoning Attack algorithm, which could hijack the clean tree-based X-armed bandits algorithm, i.e., high confidence tree (HCT) and make it choose the nodes including the arm targeted by the attacker very frequently with a sub-linear attack cost, i.e., O(Tα)(0 © 2022 IEEE.

关键词： stochastic systems

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DroBoost: An intelligent Score and Model Boosting Method for Drone Detection 21st

DroBoost: An Intelligent Score and Model Boosting Method for...

引用

21st international conference on Image Analysis and Processing (ICIAP)

作者： Eryuksel, Ogulcan Ozfuttu, Kamil Anil Akyon, Fatih Cagatay Sahin, Kadir Buyukborekci, Efe Cavusoglu, Devrim Altinuc, Sinan OBSS Technol OBSS AI 1606 Cad 4-1-307 Cyberpk Cyberplaza C Blok 3 Kat Ankara Turkey

ISBN: (纸本)9783031133244;9783031133237

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).

关键词： Drone detection Deep learning Object tracking Object detection Synthetic data

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Multi-Label kNN classifier with Online Dual Memory on data stream 21

Multi-Label kNN classifier with Online Dual Memory on data s...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Wang, Xihui Kuntz, Pascale Meyer, Frank Lemaire, Vincent Lab Digital Sci Nantes Nantes France Orange Labs Lannion France

ISBN: (纸本)9781665424271

Due to an ever-increasing demand for analyzing the large volumes of information issuing from high-speed data streams, multi-label stream classification is replacing the traditional offline multi-label classification system and has thus become a focal point in recent years. In this paper, we propose a new algorithm for multi-label stream classification that relies on an Online Dual Memory (ODM). It uses a short-term memory in order to adapt quickly to changes within the data stream, and employs a long-term memory for recurring concepts. Furthermore, via a modification of learning Vector Quantization, ODM is not only capable of classifying efficiently but also of maintaining a low computational complexity. Experiments were conducted so as to establish a comprehensive overview of its performance, which was achieved by comparing four methods on two different kinds of datasets (nine stationary datasets and four datasets with concept drift). Our results confirm the high accuracy and low time complexity of ODM, as well as its ability to cope with concept drift.

关键词： supervised learning multi-Label classification data stream online memory

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Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis 21

Towards Interpretability and Personalization: A Predictive F...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Li, Yang Zhang, Xianli Qian, Buyue Gao, Zeyu Guan, Chong Zheng, Yefeng Zheng, Hansen Wu, Fenglang Li, Chen Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Tencent Jarvis Lab Shenzhen Peoples R China Xi An Jiao Tong Univ Affiliated Hosp 1 Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt Xian Shaanxi Peoples R China

ISBN: (纸本)9781665423984

Clinical time-series is receiving long-term attention in data mining and machine learning communities and has boosted a variety of data-driven applications. Identifying similar patients or subgroups from clinical time-series is an essential step to design tailored treatments in clinical practice. However, most of the existing methods are either purely unsupervised that tend to neglect the patient outcome information or cannot generate personalized patient representation through supervised learning, thus may fail to identify 'truly similar patients' (i.e., patients who similar in both outcomes and individual outcome-related clinical variables). To tackle these limitations, we propose a novel predictive clinical time-series analysis framework. Specifically, our framework uses task-specific information to rule out the task-irrelevant factors in each patient data individually and generates the contribution scores that reveal the factors' importance for the patient outcome. Then a patient representation construction method is proposed to generate task-related and personalized representations by combining remained factors and their contribution scores. At last, similarity measurement or cluster analysis can be conducted. We evaluate our framework on three real-world clinical time-series datasets, empirically demonstrate that our framework achieves improvements in prediction performance, similarity measurement, and clustering, thus potentially benefiting patient-similarity-based precision medicine applications.

关键词： Health informatics Clinical time-series analysis Deep learning

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Gated Information Bottleneck for Generalization in Sequential Environments 21

Gated Information Bottleneck for Generalization in Sequentia...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Alesiani, Francesco Yu, Shujian Yu, Xi NEC Labs Europe Heidelberg Germany UiT Arctic Univ Norway Tromso Norway Xi An Jiao Tong Univ Xian Peoples R China Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9781665423984

Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications. In this work, we propose a new neural network-based IB approach, termed gated information bottleneck (GIB), that dynamically drops spurious correlations and progressively selects the most task-relevant features across different environments by a trainable soft mask (on raw features). GIB enjoys a simple and tractable objective, without any variational approximation or distributional assumption. We empirically demonstrate the superiority of GIB over other popular neural network-based IB approaches in adversarial robustness and out-of-distribution (OOD) detection. Meanwhile, we also establish the connection between ID theory and invariant causal representation learning, and observed that GIB demonstrates appealing performance when different environments arrive sequentially, a more practical scenario where invariant risk minimization (IRM) fails.

关键词： Adversarial Attack Causal learning Deep learning Generalization Information Bottleneck Information Theory Out of distribution Sequential learning

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Self-supervised Prototype Conditional Few-Shot Object Detection 21st

Self-supervised Prototype Conditional Few-Shot Object Detect...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Kobayashi, Daisuke Toshiba Co Ltd Corp Res & Dev Ctr Kawasaki Kanagawa Japan

ISBN: (纸本)9783031064302;9783031064296

Traditional deep learning-based object detection methods require a large amount of annotation for training, and creating such a dataset is expensive. Few-shot object detection which detects a new category of objects with a small amount of data is a difficult task. Many previous methods have been applied to detect new categories with few data by fine-tuning. However, fine-tuning methods cannot be sufficiently adapted quickly to new environments because of time-consuming training. In this study, we develop methods that can be adapted to new categories without fine-tuning. A prototype conditional detection module built on prototypes for each category is intended to be conditioned and detected by prototypes. Moreover, a self-supervised conditional detection module improves the representational capability for detecting new categories by a self-supervised task that uses embeddings outside the annotation region to detect the same regions before and after image transformation. The results of extensive experiments on the PASCAL VOC and MS COCO show the effectiveness of the proposed method.

关键词： Few-shot object detection Few-shot learning Object detection

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Improving Identification of Defective Wafer Maps by data Augmentation via Enhanced CycleGAN

Improving Identification of Defective Wafer Maps by Data Aug...

引用

international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Lamia Alam Nasser Kehtarnavaz Department of Electrical and Computer Engineering University of Texas at Dallas Texas USA

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

In integrated circuit manufacturing, a wafer map represents a pattern of defective dies or chips on a wafer. In order to identify different defective wafer maps or patterns by a deep learning model, it is essential that a balanced number of samples of different patterns of wafer maps is used to train the model. In this paper, the Enhanced CycleGAN generative network is used to generate realistic defective wafer maps for which adequate numbers of samples do not exist. The identification of defective wafer maps is then carried out without and with such a data augmentation. Based on the public domain wafer map dataset of WM-811K, it is shown that our data augmentation improves the overall identification accuracy by 6% and 38% for the two test cases examined compared to the no data augmentation cases.

关键词： Semiconductor device modeling Deep learning Measurement Accuracy Electrical engineering computing data augmentation data models Manufacturing Integrated circuit modeling standards

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An OPC UA-based industrial Big data architecture

An OPC UA-based industrial Big Data architecture

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IEEE international conference on Industrial Informatics (INDIN)

作者： Eduard Hirsch Simon Hoher stefan Huber JR Centre for Intelligent and Secure Industrial Automation Salzburg University of Applied Sciences Puch Austria

Industry 4.0 factories are complex and data-driven. data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations.

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Incomplete Multi-view Multi-label Active learning 21

Incomplete Multi-view Multi-label Active Learning

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Qu, Chuanwei Wang, Kuangmeng Zhang, Hong Yu, Guoxian Domeniconi, Carlotta Southwest Univ Coll Comp & Informat Sci Chongqing Peoples R China Shandong Univ Sch Software Jinan Peoples R China George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

ISBN: (纸本)9781665423984

The label information of training data is crucial for effective machine learning in many domains, while it is expensive to annotate data at a large-scale by domain experts. The problem was intensified by the multiplicity and incompleteness of multiview multi-label (MVML) objects, which is ignored by almost all existing multi-view multi-label active learning approaches. In this paper, we propose an incomplete multi-view multi-label active learning (iMVMAL) approach to reduce the cost of querying MVML data. iMVMAL firstly extends under-complete Autoencoder to learn the shared/individual representations of samples across/within incomplete views by an indicator matrix to indicate the missing samples of respective view. As such, the optimization of the Autoencoder's parameters will not be impacted by the missing samples. Next, it uses the extracted shared/individual information to train multiple classifiers and to quantify the informativeness of sample-label pairs from these classifiers, from label-wise and sample-wise information also. After that, it selects the sample-label pairs with the highest informativeness for query. Empirical studies on benchmark datasets show that iMVMAL outperforms competitive baselines at the same query cost in the complete multi-view setting, and maintains its effectiveness in the incomplete multi-view setting as well.

关键词： Incomplete multi-view learning Active learning Multi-label learning Commonality and individuality

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