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Demsasa: micro-video scene classification based on denoising multi-shots association self-attention

PATTERN ANALYSIS AND APPLICATIONS 2024年第4期27卷 1-9页

作者： Gong, Rui Zhang, Yu Zhang, Yanhui Liu, Yue Guo, Jie Nie, Xiushan Shandong Jianzhu Univ Jinan 250101 Peoples R China Shandong First Med Univ Affiliated Hosp 1 Jinan 250014 Peoples R China

Due to segmentation and splicing in micro-videos when user upload videos to platform, the content of different shots in the same scene is discontinuous, which leads to the problem of large content differences between different shots. At the same time, due to the low resolution of the shooting equipment or jitter and other factors, the video has noise information. In view of the above problems, the conventional and serialized scene feature learning in micro-video cannot learn the content difference and correlation between different shots, which will weaken the semantic representation of scene features. Therefore, this paper proposes a micro-video scene classification method based on De-noising Multi-shots Association Self-attention (DeMsASa) model. In this method, the shot boundary detection algorithm segments micro- video firstly, and then the semantic representation of the multi-shots video scene is learned by de-noising, association between video frames in the same shot and the association modeling between different shots. Experiments results show that the classification performance of the proposed method is superior to the existing micro-video scene classification methods.

关键词： Micro-video scene classification Multi-shots association self-attention Shot boundary detection denoising autoencoder

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Multi-feature fusion deep networks

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NEUROCOMPUTING 2016年 218卷 164-171页

作者： Ma, Gang Yang, Xi Zhang, Bo Shi, Zhongzhi Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China

In this paper, we propose a novel deep networks, multi-feature fusion deep networks (MFFDN), based on denoising autoencoder. MFFDN significantly reduces the classification error while giving the interpretability of the hidden-layer feature representation in learning process. Comparing with the traditional denoising autoencoder, MFFDN mainly shows the following advantages: (1) minimally retaining a certain amount of "information" constrained to a given form about its input;(2) explicitly interpreting the meaning of the feature representation in one hidden layer;(3) enhancing discriminativeness of the whole networks. At last, the experiments analysis on MNIST, CIFAR-10 and SVHN prove the state-of-the-art performance improvement of MFFDN with the advantages minimally retaining "information" constraint and the interpreted hidden feature representation. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Deep networks denoising autoencoder Interpretability Discriminativeness

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Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae102页

作者： Hu, Dayu Liang, Ke Dong, Zhibin Wang, Jun Zhao, Yawei He, Kunlun Natl Univ Def Technol NUDT Changsha Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Big Data Res Ctr 28 Fuxing Rd Beijing 100853 Peoples R China

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://***/DayuHuu/scEMC.

关键词： single-cell clustering skip aggregation network denoising autoencoder ZINB deep learning

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Multi-condition training for noise-robust speech emotion recognition

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ACOUSTICAL SCIENCE AND TECHNOLOGY 2019年第6期40卷 406-409页

作者： Chiba, Yuya Nose, Takashi Ito, Akinori Tohoku Univ Grad Sch Engn Aoba Ku 6-6-05 Aza Aoba Sendai Miyagi 9808579 Japan

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Efficient ensemble to combat flash attacks

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COMPUTATIONAL INTELLIGENCE 2024年第1期40卷

作者： Kumar, Om C. U. Bhama, Ponsy R. K. Sathia Easwari Engn Coll Dept CSE Chennai 600089 Tamil Nadu India Anna Univ Dept Comp Technol MIT Campus Chennai Tamil Nadu India

Flash event generates enormous traffic and the cloud service providers use sustaining techniques like scaling and content delivery network to up their services. One of the main bottlenecks that the cloud service providers still find difficult to tackle is flash attacks. Illegitimate users send craftily designed packets to land up inside the server for wreaking havoc. As deep learning autoencoder has the potential to detect malicious traffic it has been used in this research study to develop an ensemble. Convolutional neural network is efficacious in overcoming the issue of overfitting;deep autoencoder is proficient in extracting features through dimensionality reduction. In order to obtain both these advantages it was decided to develop an ensemble keeping denoising autoencoder as the core element. The process of addressing a flash attack requires first detecting the presence of bot in malicious traffic, second studying its nature by observing its behavioral manifestations. Detection of botnet was achieved by three ensembles, namely, DAE_CNN, DAE_MLP, and DAE_XGB. But capturing its external manifested behavior is challenging, because the bot signatures are always in a state of flux. The simulated empirical study yielded an appreciable outcome. Its accuracy rate was 99.9% for all the three models and the false positive rates were 0, 0.006, and 0.001, respectively.

关键词： botnet convolutional neural network denoising autoencoder DDoS ensemble multilayer perceptron

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Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors

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SENSORS 2020年第7期20卷 1895-1895页

作者： Sigcha, Luis Costa, Nelson Pavon, Ignacio Costa, Susana Arezes, Pedro Manuel Lopez, Juan De Arcas, Guillermo Univ Politecn Madrid Grp Invest Instrumentac & Acust Aplicada I2A2 ETSI Ind Campus Sur UPMCtra ValenciaKm 7 Madrid 28031 Spain Univ Minho Sch Engn ALGORITMI Res Ctr P-4800058 Guimaraes Portugal

Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).

关键词： IMU accelerometer convolutional neural networks LSTM consecutive windows denoising autoencoder time distributed spectral representation

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Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Zhang, Lin Wang, Jixin Chang, Rui Wang, Weigang Zhejiang Hosp Integrated Tradit Chinese & Western Hangzhou 310003 Peoples R China Zhejiang Gongshang Univ Dept Stat & Math Hangzhou 310018 Peoples R China Jining 1 Peoples Hosp Dept ICU Jining 272100 Peoples R China

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.

关键词： Hepatitis C autoencoder denoising autoencoder

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CDEC:a constrained deep embedded clustering

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International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 686-701页

作者： Elham Amirizadeh Reza Boostani Department of Computer Sciences and Engineering Shiraz UniversityShirazIran

Purpose-The aim of this study is to propose a deep neural network(DNN)method that uses side information to improve clustering results for big datasets;also,the authors show that applying this information improves the performance of clustering and also increase the speed of the network training ***/methodology/approach-In data mining,semisupervised learning is an interesting approach because good performance can be achieved with a small subset of labeled data;one reason is that the data labeling is expensive,and semisupervised learning does not need all *** type of semisupervised learning is constrained clustering;this type of learning does not use class labels for ***,it uses information of some pairs of instances(side information),and these instances maybe are in the same cluster(must-link[ML])or in different clusters(cannot-link[CL]).Constrained clustering was studied extensively;however,little works have focused on constrained clustering for big *** this paper,the authors have presented a constrained clustering for big datasets,and the method uses a *** authors inject the constraints(ML and CL)to this DNN to promote the clustering performance and call it constrained deep embedded clustering(CDEC).In this manner,an autoencoder was implemented to elicit informative low dimensional features in the latent space and then retrain the encoder network using a proposed Kullback-Leibler divergence objective function,which captures the constraints in order to cluster the projected *** proposed CDEC has been compared with the adversarial autoencoder,constrained 1-spectral clustering and autoencoder t k-means was applied to the known MNIST,Reuters-10k and USPS datasets,and their performance were assessed in terms of clustering *** results confirmed the statistical superiority of CDEC in terms of clustering accuracy to the ***-First of all,this is the first DNN-constrained clustering that

关键词： Deep neural networks Clustering Constrained clustering Big data denoising autoencoder Kullback-Leibler divergence Constrained deep embedded clustering(CDEC)

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SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement 17

SNR-Aware Convolutional Neural Network Modeling for Speech E...

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17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Fu, Szu-Wei Tsao, Yu Lu, Xugang Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Inst Informat & Commun Technol Kyoto Japan

ISBN: (纸本)9781510833135

This paper proposes a signal-to-noise-ratio (SNR) aware convolutional neural network (CNN) model for speech enhancement (SE). Because the CNN model can deal with local temporal-spectral structures of speech signals, it can effectively disentangle the speech and noise signals given the noisy speech signals. In order to enhance the generalization capability and accuracy, we propose two SNR-aware algorithms for CNN modeling. The first algorithm employs a multi -task learning (MTL) framework, in which restoring clean speech and estimating SNR level are formulated as the main and the secondary tasks, respectively, given the noisy speech input. The second algorithm is an SNR adaptive denoising, in which the SNR level is explicitly predicted in the first step, and then an SNR-dependent CNN model is selected for denoising. Experiments were carried out to test the two SNR-aware algorithms for CNN modeling. Results demonstrate that CNN with the two proposed SNR-aware algorithms outperform the deep neural network counterpart in terms of standardized objective evaluations when using the same number of layers and nodes. Moreover, the SNR-aware algorithms can improve the de noising performance with unseen SNR levels, suggesting their promising generalization capability for real-world applications.

关键词： speech enhancement convolutional neural network denoising autoencoder multi-task learning

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Denoised Labels for Financial Time Series Data via Self-Supervised Learning 22

Denoised Labels for Financial Time Series Data via Self-Supe...

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3rd ACM International Conference on AI in Finance (ICAIF)

作者： Ma, Yanqing Ventre, Carmine Polukarov, Maria Kings Coll London London England

ISBN: (纸本)9781450393768

The introduction of electronic trading platforms effectively changed the organisation of traditional systemic trading from quote-driven markets into order-driven markets. Its convenience led to an exponentially increasing amount of financial data, which is however hard to use for the prediction of future prices, due to the low signal-to-noise ratio and the non-stationarity of financial time series. Simpler classification tasks - where the goal is to predict the directions of future price movement via supervised learning algorithms need sufficiently reliable labels to generalise well. Labelling financial data is however less well defined than in other domains: did the price go up because of noise or a signal? The existing labelling methods have limited countermeasures against the noise, as well as limited effects in improving learning algorithms. This work takes inspiration from image classification in trading [6] and the success of self-supervised learning in computer vision (e.g., [16]). We investigate the idea of applying these techniques to financial time series to reduce the noise exposure and hence generate correct labels. We look at label generation as the pretext task of a self-supervised learning approach and compare the naive (and noisy) labels, commonly used in the literature, with the labels generated by a denoising autoencoder for the same downstream classification task. Our results demonstrate that these denoised labels improve the performances of the downstream learning algorithm, for both small and large datasets, while preserving the market trends. These findings suggest that with our proposed techniques, self-supervised learning constitutes a powerful framework for generating "better" financial labels that are useful for studying the underlying patterns of the market.

关键词： Self-Supervised Learning denoising autoencoder Financial Labels Directional-Change Events

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