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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Chengyong Liu Yanchao Zhao Kun Zhu Sheng Zhang Jie Wu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key laboratory for Novel Software Technology Nanjing University China Department of Computer and Information Sciences Temple University USA

ISBN: (纸本)9781728125848

Signal map is of great importance, especially in the dawn of 5G network, for site spectrum monitoring, location-based services (LBS), network construction, and cellular planning. Despite its significance, the traditional signal map construction, e.g., through full site survey, could be time-consuming and labor-intensive as the signal varies frequently over time and the accuracy requirement grows rapidly with the emergence of new applications. Even with crowdsourcing scheme, the participants tend to be unevenly distributed in space while the encouragement budgets for the participants could be far from enough to collect adequate high-quality measurements. Therefore, the signal map constructed by crowdsourcing is often sparse and incomplete. To this end, in this paper, we study how to effectively reconstruct and update the signal map in the case of partially measured signal maps with minimum cost and propose an auto-encoder-based active signal map reconstruction method (AER). Our method is mainly innovative in three parts. Firstly, AER can effectively update the signal map with only a small number of observations while also fully using the incomplete historical signals to effectively update the signal map online. Secondly, AER consists of an active query mechanism which quantitatively evaluates the most valuable measurement site for reconstruction, which further reduces the measurement cost to a large extent. Thirdly, to cope with the measurement dynamics, we give a new signal map model describing not only the signal strength but also the signal dynamics, based on which an advanced AER algorithm is proposed. The simulation results demonstrate the advantages and effectiveness of our approach in both accuracy and cost.

关键词： Signal map Active matrix completion autoencoder Crowdsourcing Signal mapping signals maps MAPPING(MATHEMATICAL) Crowdsource ANION EXCHANGE RESIN Site surveys required precision

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Learning for Device Pairing in Body Area Networks 18

Learning for Device Pairing in Body Area Networks

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Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

作者： Yuezhong Wu Wen Hu Mahbub Hassan University of New South Wales

ISBN: (纸本)9781450359528

Recent work has shown that it is possible for two wearable devices worn by the same user to generate a common key for secure pairing by exploiting gait as a common secret. A key challenge for such device pairing lies in matching the bits of the keys generated by two independent devices despite the noisy on-board sensor measurements. We propose a novel machine learning framework that uses an autoencoder to help one device predict the sensor observations at another device and generate the key using the predicted sensor data. We prototype the proposed method and evaluate it using real subjects. Our results show that the proposed method achieves a 10% increase in bit agreement rate between two keys generated independently by two different wearable devices.

关键词： Cryptographic Key Device Pairing autoencoder

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Optimization of Generalization Problem based on Mean Teacher Model

Optimization of Generalization Problem based on Mean Teacher...

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第43届中国控制会议

作者： Zhiqi Long Wenjie Chen Jiayi Lin National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper focuses on optimizing the Mean Teacher model and validating the improved classification performance on the benchmark CIFAR-10 dataset. The paper optimizes the loss function by constructing a teacher graph, incorporating consistency loss, and introducing smooth neighbors based on the teacher graph. Simultaneously, the student and teacher networks of the original model are replaced with autoencoders to enhance prediction accuracy through the encoder's classification and reconstruction abilities. Ultimately, two ConvLarge structure algorithms, SNTG(Smooth Neighbors on Teacher) and HybridNet, are developed. These three algorithms are compared for recognition performance on the CIFAR-10 dataset, achieving promising results. Both SNTG and HybridNet significantly improve model accuracy compared to the original Mean Teacher algorithm,reducing recognition error rates to around 17% and increasing the accuracy by 3.5%.

关键词： Semi-Supervised Learning Image Classification Mean Teacher autoencoder

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Automatic Encoder Combined with Nasnet in Histopathologic Cancer Detection

Automatic Encoder Combined with Nasnet in Histopathologic Ca...

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IEEE International Conference on Computer and Communications

作者： Lu Liu Xianzhong Liu Schoo of Sofwre Engineering Es Chin Nor UniversiySchoo of Sofwre Engineering Es Chin Nor Universiy Sofwre/Hrdwre Co-design Engineering Reserch Cener MoE

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

Computer vision is a major branch of artificial intelligence algorithm. The algorithm of computer vision mainly consists of the processing of image and video, including image recognition and image detection etc. Practice has proved that computer vision is scientific and practical to a certain extent. In pace with the development of in-depth learning, computer vision has already been put to use well in all walks of life. However, it is still in exploring stage in the medical field, because the medical data is sensitive, which requires high accuracy of the algorithm. In this paper, images of PCam medical electron microscope are put to use for tumor detection, which is an task of image recognition and an automatic encoder is used to lower the dimensions of the data into low-dimensional vectors which are used as features in training. Then the vectors are added as features to the training, and the model is trained together with the original data set as the training features of NASnet. Because detection algorithms in the medical field pay more importance to the true positive rate and false positive rate. When the output is positive, it is necessary to be revalidated by SVM model trained by encoder. As a result, ROC curve is 0.98, which is 0.03 higher than Baseline.

关键词： Cancer detection Nasnet autoencoder Artifitial intelligence

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A Feature Extraction Method Using Multiscale CLAE for Intelligent Fault Diagnosis

A Feature Extraction Method Using Multiscale CLAE for Intell...

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第40届中国控制会议

作者： Ziqi Hao Qingyu Yang Pengtao Song School of Automation Science and Engineering Xi'an Jiaotong University SKLMSE lab School of Automation Science and EngineeringXi'an Jiaotong University

In order to avoid the occurrence of large-scale accidents to the greatest extent,it is necessary to find out the faults in the continuous operation of mechanical equipment and make corresponding *** the development of artificial intelligence technology,autoencoder（AE） has been widely used in fault diagnosis,among which class level autoencoder（CLAE）effectively overcome the intra-class variations of detection data under different ***,the CLAE model just uses a single scale method to extract features from the data,and when the data is mixed with noise,the classification performance will *** this work,we proposed a multiscale class level autoencoder model（MSCLAE） which aims at learning robust and discriminative ***,we extract single-scale features from each CLAE model with different input and ouput dimensions respectively and combine these features for fault pattern *** experimental results on a motor bearing dataset have demonstrated that the proposed method can extract more robust features and obtain better anti-noise ability.

关键词： Representation Learning Features Fusion autoencoder Multiscale

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Video Inter-frame Tampering Detection Based on SN-VGG+BiLSTM-AE Composite Model 22

Video Inter-frame Tampering Detection Based on SN-VGG+BiLSTM...

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Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City

作者： Qi Xing Yun Luo Zhaoyuan Zhang Fan Zhang Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450397438

Video inter-frame tampering detection is the most common type of forensics in video forensics. The traditional detection method is to detect tampering by extracting digital image features of video frames, such as SIFT, HOG, and ORB. The accuracy of frame discrimination and localization is limited. This paper introduces deep learning into the problem of tampering detection, and proposes a composite network model structure using the Siamese network Siamese and the bidirectional long short-term memory network autoencoder BiLSTM autoencoder to detect tampered frames. Among them, Siamese calculates the inter-frame distance by calculating the depth features of the frames extracted by VGG-16, and inputs them into BiLSTM autoencoder for frame sequence anomaly detection and localization. The model is experimented on two different datasets with good results, validating the model generalization performance. Compared with the classical method, this model obtains higher precision(93.7%) of tamper points, which verifies the superiority of this deep learning model.

关键词： deep learning Siamese video inter-frame tampering video forensics autoencoder BiLSTM

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Performance Comparison of Network Intrusion Detection System Based on Different Pre-processing Methods and Deep Neural Network

Performance Comparison of Network Intrusion Detection System...

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Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence

作者： Gaurav Meena Babita Dhanwal Mehul Mahrishi Kamal Kant Hiran Central University of Rajasthan Ajmer Rajasthan India Swami Keshvanand Institute of Technology Jaipur India Sir Padmapat Singhania University Udaipur India

ISBN: (纸本)9781450387637

There is a crucial need to have an intelligent and effective intrusion detection system to overcome network intrusion and cyber security attacks. Through this paper, the author compares various data pre-processing methods categorized as Feature selection, Feature encoding, and Feature scaling. The pre-processed data and an autoencoder are used for further processing to get the best features and use them with a deep neural network for classification. Finally, the paper concludes a comparative analysis of pre-processing methods to determine the best for performing network intrusion detection.

关键词： autoencoder Deep learning Feature encoding Feature scaling Feature selection Intrusion detection Neural network

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R~2FP: Rich and Robust Feature Pooling for Mining Visual Data

R~2FP: Rich and Robust Feature Pooling for Mining Visual Dat...

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IEEE International Conference on Data Mining

作者： Wei Xiong Bo Du Lefei Zhang Ruimin Hu Wei Bian Jialie Shen Dacheng Tao School of Computer Science Wuhan University National Engineering Research Center for MultimediaSoftware Luojiashan Centre for Quantum Computation & Intelligent Systems University of Technology School of Information Systems Singapore Management University

ISBN: (纸本)9781467395052

The human visual system proves smart in extracting both global and local features. Can we design a similar way for unsupervised feature learning? In this paper, we propose a novel pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R~2FP), to better explore rich and robust representation from sparse feature maps of the input data. Both local and global pooling strategies are further considered to instantiate such a method and intensively studied. The former selects the most conductive features in the sub-region and summarizes the joint distribution of the selected features, while the latter is utilized to extract multiple resolutions of features and fuse the features with a feature balancing kernel for rich representation. Extensive experiments on several image recognition tasks demonstrate the superiority of the proposed techniques.

关键词： Pooling autoencoder Representation learning

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3D点云形状补全GAN

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计算机科学 2021年第4期48卷 192-196页

作者：赵新灿常寒星金仁标郑州大学信息工程学院郑州450001

在真实的扫描环境中,由于视线遮挡或技术人员操作不当,实际采集到的点云模型会存在形状不完整的问题。点云模型的不完整性会对后续应用产生严重的影响,因此提出3D点云形状补全GAN用于完成点云模型的形状补全。该网络的点云重建部分由Poi... 详细信息

在真实的扫描环境中,由于视线遮挡或技术人员操作不当,实际采集到的点云模型会存在形状不完整的问题。点云模型的不完整性会对后续应用产生严重的影响,因此提出3D点云形状补全GAN用于完成点云模型的形状补全。该网络的点云重建部分由PointNet中用于数据对齐的T-Net结构与3D点云autoencoder网络相结合,来完成预测和填充缺失数据,识别器采用3D点云autoencoder中的Encoder部分对补全3D点云数据与真实的3D点云数据进行区分。最后,在ShapeNet数据集中训练上述网络结构,对所训练的网络模型进行验证并与其他基准方法进行定性比较。从实验结果可以看出,3D点云形状补全GAN可以将具有缺失数据的点云模型补全为完整的3D点云。在ShapeNet的3个子数据集chair,table以及bed上,相比基于3D点云autoencoder的方法,所提方法的F 1分数分别提高了3.0%,3.3%以及3.1%,相比基于体素3D-EPN的方法,所提方法的F 1分数分别提高了9.9%,5.8%以及4.3%。

关键词： 3D点云形状补全 autoencoder 生成对抗网络

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Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology

Data Integration Using Advances in Machine Learning in Drug ...

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作者： Irene Lena Hudson

While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery. The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand–protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learni

关键词： Deep learning Machine Learning Artificial Intelligence Data integration Drug discovery Chemistry Network biology Embeddings Deep neural network Recurrent neural network autoencoder Convolution graph network

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