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Few Shot Scene Classification in Remote Sensing using Meta-Agnostic Machine

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Few Shot Scene Classification in Remote Sensing using Meta-A...

Conference on Data Science and Machine Learning Applications (CDMA)

作者： Dalal A. Alajaji Haikel Alhichri Department of Computer Engineering King Saud University Riyadh Saudi Arabia Department of Computer Engineering Advanced Lab for Intelligent Systems Research (ALISR) King Saud University Riyadh Saudi Arabia

ISBN: (数字)9781728127460

ISBN: (纸本)9781728127477

Scene classification has become an important research topic in remote sensing (RS) field. The typical solution relies on labeling a large enough set of the RS scenes manually using expert opinion, then training the algorithm on this set to learn how to classify other new scenes correctly. The best performance deep learning models required a large labeled dataset for training. Accordingly, there is a great need to develop an intelligent machine learning algorithm that can learn to classify RS datasets containing new unseen classes from a few labeled samples only, which is known as few-shot machine learning. In this work, we develop a deep few-shot learning method for the classification of RS scenes. The proposed method is based on Model Agnostic Meta-Learning (MAML), one of the recently introduced and most popular meta-learning algorithms. In this paper, we report preliminary results using the UC Merced, OPTIMAL-31, and AID RS scene datasets.

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Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders 23rd

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Abnormality Detection in Chest X-Ray Images Using Uncertaint...

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Mao, Yifan Xue, Fei-Fei Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi Liu, Hongmei School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Pazhou Lab Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783030597245

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

A Fog Based Approach for Hazards Differentiation in an IIoT Scenario

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A Fog Based Approach for Hazards Differentiation in an IIoT ...

International Conference on computers, Communications, and Systems (ICCCS)

作者： Azin Moradbeikie Kamal Jamshidi Ali Bohlooli Jordi Garcia Xavi Masip-Bruin Faculty of Computer Engineering University of Isfahan Isfahan Iran Advanced Network Architectures Lab (CRAAX) UPC BarcelonaTech Vilanova Spain

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Industrial control systems (ICS) are applied in many critical infrastructures. Reducing reconfiguration time after hazard leads to safety improvement, so it is one of the most important objectives in these systems. Hazards can be due to the “system failure” or “cyber-attacks” factors. One of the procedures that can reduce the reconfiguration time is determining as soon as possible the cause of hazards based on the above mentioned factors. Differentiation of attack from failure without redundant data in addition to data from the system sensors is not possible. With advent of the IoT as IIoT, a condition is developed to provide the required redundant data; however, by increasing the number of IIoT devices within a factory, the generated data volume becomes too large. In this paper we describe a fog-based approach applied in a factory to deal with such increasing complexity. We compare the proposed method with a traditional cloud-based solution. According to the results, the proposed method leads to a reduction of 60% lost time in the recovery reconfiguration step of the system.

关键词： Hazards Cloud computing Sensor systems Integrated circuits Production facilities

Design and implementation of a maxi-sized mobile robot (Karo) for rescue missions

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arXiv 2020年

作者： Habibian, Soheil Dadvar, Mehdi Peykari, Behzad Hosseini, Alireza Salehzadeh, M. Hossein Hosseini, Alireza H.M. Najafi, Farshid Advanced Mobile Robotics Lab Qazvin Azad University Iran Department of Electrical and Computer engineering Qazvin Azad University Iran

Rescue robots are expected to carry out reconnaissance and dexterity operations in unknown environments comprising unstructured obstacles. Although a wide variety of designs and implementations have been presented within the field of rescue robotics, embedding all mobility, dexterity, and reconnaissance capabilities in a single robot remains a challenging problem. This paper explains the design and implementation of Karo, a mobile robot that exhibits a high degree of mobility at the side of maintaining required dexterity and exploration capabilities for urban search and rescue (USAR) missions. We first elicit the system requirements of a standard rescue robot from the frameworks of Rescue Robot League (RRL) of RoboCup and then, propose the conceptual design of Karo by drafting a locomotion and manipulation system. Considering that, this work presents comprehensive design processes along with detail mechanical design of the robot’s platform and its 7-DOF manipulator. Further, we present the design and implementation of the command and control system by discussing the robot’s power system, sensors, and hardware systems. In conjunction with this, we elucidate the way that Karo’s software system and human-robot interface are implemented and employed. Furthermore, we undertake extensive evaluations of Karo’s field performance to investigate whether the principal objective of this work has been satisfied. We demonstrate that Karo has effectively accomplished assigned standardized rescue operations by evaluating all aspects of its capabilities in both RRL’s test suites and training suites of a fire department. Finally, the comprehensiveness of Karo’s capabilities has been verified by drawing quantitative comparisons between Karo’s performance and other leading robots participating in RRL. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

Optimization of institutional incentives for cooperation in structured populations

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arXiv 2023年

作者： Wang, Shengxian Chen, Xiaojie Xiao, Zhilong Szolnoki, Attila Vasconcelos, Vítor V. School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Institute of Technical Physics and Materials Science Centre for Energy Research P.O. Box 49 BudapestH-1525 Hungary Computational Science Lab Informatics Institute University of Amsterdam Amsterdam1098XH Netherlands Institute for Advanced Study University of Amsterdam Amsterdam1012 GC Netherlands

The application of incentives, such as reward and punishment, is a frequently applied way for promoting cooperation among interacting individuals in structured populations. However, how to properly use the incentives is still a challenging problem for incentive-providing institutions. In particular, since the implementation of incentive is costly, to explore the optimal incentive protocol, which ensures the desired collective goal at a minimal cost, is worthy of study. In this work, we consider the positive and negative incentives respectively for a structured population of individuals whose conflicting interactions are characterized by a prisoner’s dilemma game. We establish an index function for quantifying the cumulative cost during the process of incentive implementation, and theoretically derive the optimal positive and negative incentive protocols for cooperation on regular networks. We find that both types of optimal incentive protocols are identical and time-invariant. Moreover, we compare the optimal rewarding and punishing schemes concerning implementation cost and provide a rigorous basis for the usage of incentives in the game-theoretical framework. We further perform computer simulations to support our theoretical results and explore their robustness for different types of population structures, including regular, random, small-world, and scale-free networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Game theory

Non-isomorphic inter-modality graph alignment and aynthesis for holistic brain mapping

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arXiv 2021年

作者： Mhiri, Islem Nebli, Ahmed Mahjoub, Mohamed Ali Rekik, Islem Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Brain graph synthesis marked a new era for predicting a target brain graph from a source one without incurring the high acquisition cost and processing time of neuroimaging data. However, works on recovering a brain graph in one modality (e.g., functional brain imaging) from a brain graph in another (e.g., structural brain imaging) remain largely scarce. Besides, existing multimodal graph synthesis frameworks have several limitations. First, they mainly focus on generating graphs from the same domain (intra-modality), overlooking the rich multimodal representations of brain connectivity (inter-modality). Second, they can only handle isomorphic graph generation tasks, limiting their generalizability to synthesizing target graphs with a different node size and topological structure from those of the source one. More importantly, both target and source domains might have different distributions, which causes a domain fracture between them (i.e., distribution misalignment). To address such challenges, we propose an inter-modality aligner of non-isomorphic graphs (IMANGraphNet) framework to infer a target graph modality based on a given modality. Our three core contributions lie in (i) predicting a target graph (e.g., functional) from a source graph (e.g., morphological) based on a novel graph generative adversarial network (gGAN);(ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure;and (iii) enforcing the source distribution to match that of the ground truth graphs using a graph aligner to relax the loss function to optimize. Furthermore, to handle the unstable behavior of gGAN, we design a new Ground Truth-Preserving (GT-P) loss function to guide the non-isomorphic generator in learning the topological structure of ground truth brain graphs more effectively. Our comprehensive experiments on predicting target functional brain graphs from source morphological graphs demonstrate the outperformance of IMA

关键词： Brain mapping

Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

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arXiv 2021年

作者： Li, Longyuan Zhang, Jihai Yan, Junchi Jin, Yaohui Zhang, Yunhao Duan, Yanjie Tian, Guangjian MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China State Key Lab of Advanced Optical Communication System and Network Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Huawei Noah’s Ark Lab China

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. In addition, an aligned time coding and an auxiliary transition scheme are carefully devised for batched training on unaligned sequences. Our model can be trained effectively using stochastic variational inference and generates probabilistic predictions with Monte-Carlo simulation. Furthermore, our model produces accurate, sharp and more realistic probabilistic forecasts. We also show that modeling asynchronous event sequences is crucial for multi-horizon time-series forecasting. © 2021, CC BY.

关键词： Time series

Anomaly detection of time series with smoothness-inducing sequential variational auto-encoder

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arXiv 2021年

作者： Li, Longyuan Yan, Junchi Wang, Haiyang Jin, Yaohui State Key Lab of Advanced Optical Communication System and Network MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

Deep generative models have demonstrated their effectiveness in learning latent representation and modeling complex dependencies of time series. In this paper, we present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of multi-dimensional time series. Our model is based on Variational Auto-Encoder (VAE), and its backbone is fulfilled by a Recurrent Neural Network to capture latent temporal structures of time series for both generative model and inference model. Specifically, our model parameterizes mean and variance for each time-stamp with flexible neural networks, resulting in a non-stationary model that can work without the assumption of constant noise as commonly made by existing Markov models. However, such a flexibility may cause the model fragile to anomalies. To achieve robust density estimation which can also benefit detection tasks, we propose a smoothness-inducing prior over possible estimations. The proposed prior works as a regularizer that places penalty at non-smooth reconstructions. Our model is learned efficiently with a novel stochastic gradient variational Bayes estimator. In particular, we study two decision criteria for anomaly detection: reconstruction probability and reconstruction error. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks. © 2021, CC BY.

关键词： Recurrent neural networks

Data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive