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Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Computers, Materials & Continua 2020年第11期65卷 1467-1486页

作者： Kun Zhu Nana Zhang Qing Zhang Shi Ying Xu Wang School of Computer Science Wuhan UniversityWuhan430072China School of Information Science and Engineering Qufu Normal UniversityRizhao276826China Department of Computer Science Vrije University AmsterdamAmsterdam1081HVThe Netherlands

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as ***,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant *** addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning *** address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic *** combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a *** compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software *** experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

关键词： Software defect prediction non-linear manifold learning denoising autoencoder deep neural network loss function deep learning

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Homotopy optimisation based NMF for audio source separation

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IET SIGNAL PROCESSING 2018年第9期12卷 1099-1106页

作者： Koundinya, Sriharsha Karmakar, Abhijit CEERI CSIR Integrated Syst Grp Pilani Rajasthan India Acad Sci & Innovat Res Madras Tamil Nadu India

In this study, the authors propose a novel framework for audio source separation based on a cascaded non-negative matrix factorisation (NMF) using homotopy optimisation with perturbation and ensemble (HOPE) and denoising autoencoder. NMF using traditional optimisation has a problem of finding a global solution, and hence could not achieve complete separation of the sources from the mixture. This problem has been addressed using homotopy optimisation in this study. Subsequently, using denoising autoencoder the residual sounds that are usually observed in the separated sources are removed. The enhanced audio signals are filtered using Wiener techniques to obtain the separated signals. The homotopy-based NMF is applied for separating singing voice and drums from song samples using a single-channel mixture. The separated signals are compared with other NMF algorithms by using Blind Source Separation (BSS) Eval objective quality measures. The NMF with HOPE and denoising autoencoder is shown to provide an improvement of up to 6dB in comparison with other NMF algorithms.

关键词： audio signal processing blind source separation source separation matrix decomposition denoising autoencoder traditional optimisation complete separation separated sources enhanced audio signals separated signals homotopy-based NMF separating singing voice drums NMF algorithms homotopy optimisation-based NMF audio source separation nonnegative matrix factorisation noise figure 6 0 dB

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Denoised Residual Trace Analysis for Monitoring Semiconductor Process Faults

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2019年第3期32卷 293-301页

作者： Jang, Jaeyeon Min, Byung Wook Kim, Chang Ouk Yonsei Univ Dept Ind Engn Seoul 03722 South Korea

The detection of wafer faults in early process steps through monitoring and analyzing multivariate process trace data contribute to wafer yield improvements. Standard classification algorithms have been generally used for fault detection and classification (FDC). However, this approach can cause information loss while extracting statistical features from the trace data and cannot consider class imbalance situations where much fewer faulty wafers are generated than normal wafers. In addition, the approach does not consider normal wafer-to-wafer (W2W) variations and sensor noise inherent in the trace data. These drawbacks significantly degrade FDC performance. This paper proposes a method that builds an FDC model only with trace data of normal wafers in which W2W variations and sensor noise exist. The one-class FDC method detects the occurrence of abnormal trace patterns that cause wafer faults by removing W2W variations and sensor noise from raw traces by using denoising autoencoders, and this method finds the fault-introducing process parameters with the occurrence times. In experiments using the trace data of etch and chemical vapor deposition processes, the proposed method exhibited 1% and 6% higher performance than the best-performing method among comparison methods in terms of the geometric mean of the normal and fault detection accuracies.

关键词： Fault detection and classification denoising autoencoder sensor noise wafer-to-wafer variation multivariate trace data semiconductor manufacturing

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Fast learning in Deep Neural Networks

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NEUROCOMPUTING 2016年 171卷 1205-1215页

作者： Chandra, B. Sharma, Rajesh K. Indian Inst Technol Dept Math Comp Sci Grp Delhi India Indian Inst Technol Dept Math Delhi India

The paper aims at speeding up Deep Neural Networks (DNN) since this is one of the major bottlenecks in deep learning. This has been achieved by parameterizing the weight matrix using low rank factorization and periodic functions. By parameterization, the weight matrix is split into two matrices of smaller size of rank K with periodic functions. A shrinkage parameter has been introduced which helps in reducing the number of parameters and thus helps in increasing the speed to a great extent. Performance of the proposed parameterization is compared with standard DNN, DNN based on weight factorization alone and on periodic-bounded weights. This has been demonstrated on benchmark datasets MNIST and MNIST variants. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Deep learning Deep Neural Network denoising autoencoder

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A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

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APPLIED INTELLIGENCE 2023年第19期53卷 22682-22699页

作者： Wang, Youming Wang, Yue Xian Univ Posts & Telecommun Sch Automation Xian Key Lab Adv Control & Intelligent Proc Xian 710121 Peoples R China

Remaining useful life (RUL) prediction is significant for reliability analysis and the reduction of maintenance costs for turbofan engine systems. However, most of the existing methods capture temporal or spatial features to predict RUL, which leads to the neglect of deep spatio-temporal correlation in the prediction data. In addition, there are usually noise interference and few labels in feature extraction, which causes difficulty in designing robust RUL prediction models. To address these problems, a denoised semi-supervised model based on fully convolutional denoising autoencoder, convolutional neural network, and long short-term memory network (FCDAE-CNN-LSTM) is proposed to predict RUL, where FCDAE is constructed to denoise the original data by parameter fine-tuning and CNN-LSTM is established based on parallel connection for RUL prognosis. The denoising and generalization capabilities of the RUL prediction model are enhanced by the combination of unsupervised denoising and supervised feature extraction in the case of few labeled training data. The superior features of the method lie in that FCDAE effectively captures globalized and localized features and CNN-LSTM captures multi-layer fused spatial-temporal correlations. The Root Mean Square Error (RMSE) and Score of the proposed method on C-MAPSS dataset are 12.01, 16.62, 11.84, 18.21, and 209.44, 1466.03, 205.07, 2338.93, respectively, which have demonstrated that our method achieves the state-of-the-art performance and outperforms other models.

关键词： Convolutional neural network denoising autoencoder Long short-term memory network Remaining useful life Semi-supervised deep learning

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ADAGE signature analysis: differential expression analysis with data-defined gene sets

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BMC BIOINFORMATICS 2017年第1期18卷 1-15页

作者： Tan, Jie Huyck, Matthew Hu, Dongbo Zelaya, Rene A. Hogan, Deborah A. Greene, Casey S. Geisel Sch Med Dartmouth Dept Mol & Syst Biol Hanover NH 03755 USA Univ Penn Dept Syst Pharmacol & Translat Therapeut Philadelphia PA 19104 USA Geisel Sch Med Dartmouth Dept Microbiol & Immunol Hanover NH 03755 USA

Background: Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://***) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Delta anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results;however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and A

关键词： Gene set analysis denoising autoencoder Unsupervised feature extraction Data-defined gene set Pseudomonas Aeruginosa

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A novel U-Net with dense block for drum signal separation from polyphonic music signal mixture

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第3期17卷 627-633页

作者： George, E. Vinitha Devassia, V. P. Cochin Univ Sci & Technol Model Engn Coll Kochi Kerala India St Josephs Coll Engn & Technol Palai Kerala India

Deep neural network algorithms have shown promising results for music source signal separation. Most existing methods rely on deep networks, where billions of parameters need to be trained. In this paper, we propose a novel autoencoder framework with a reduced number of parameters to separate the drum signal component from a music signal mixture. A denoising autoencoder with a U-Net architecture and direct skip connections was employed. A dense block is included in the bottleneck of the autoencoder stage. This technique was tested on both demixing secret data (DSD) and the MUSDB database. The source-to-distortion ratio (SDR) for the proposed method was at par with that of other state-of-the-art methods, whereas the number of parameters required was quite low, making it computationally more efficient. The experiment performed using the proposed method to separate drum signal yielded an average SDR of 5.71 on DSD and 6.45 on MUSDB database while using only 0.32 million parameters.

关键词： Audio source separation Convolutional neural network Deep learning denoising autoencoder U-Net architecture

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Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

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BMC BIOINFORMATICS 2020年第1期21卷 1-13页

作者： Lou, Shaoke Li, Tianxiao Spakowicz, Daniel Yan, Xiting Chupp, Geoffrey Lowell Gerstein, Mark Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Ohio State Univ Div Med Oncol Columbus OH 43210 USA Yale Sch Med Pulm & Crit Care New Haven CT 06520 USA

BackgroundThe pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression ***, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine *** found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

关键词： Asthma Asthma subtypes denoising autoencoder Biomarker Non-invasive

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Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2020年第1期2020卷 1-26页

作者： Yen, Benjamin Hioka, Yusuke Univ Auckland Dept Mech Engn Acoust Res Ctr 20 Symonds St Auckland 1010 New Zealand

A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064-0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037-0.185 decrease in the 0.75 quartile haversine distance error.

关键词： Microphone array Unmanned aerial vehicle Rotor noise Source localisation denoising autoencoder Power spectral density Restricted peak search

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Deep Learning Modeling for Top-N Recommendation With Interests Exploring

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IEEE ACCESS 2018年 6卷 51440-51455页

作者： Zhou, Wang Li, Jianping Zhang, Malu Wang, Yazhou Shah, Fadia Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Optoelect Informat Chengdu 610054 Sichuan Peoples R China

Recommender systems (RS) currently play a crucial role in information filtering and retrieval, and have been ubiquitously applied in many domains, although suffering from such data sparsity and cold start problems. There are plenty of studies that try to make efforts to improve the performance of RS through different aspects, such as traditional matrix factorization technique and deep learning methods in recent years, however, it's still a challenging issue under research. In this paper, motivated by this, a two-stage deep learning-based model for top-N recommendation with interests exploring (DLMR) is proposed: 1) DLMR explores latent interests for each user, captures factors from reviews and contextual information via convolutional neural network, and performs convolutional matrix factorization to generate the candidates list;2) In order to enhance the recommendation performance, DLMR further conducts candidates ranking through a three-layer denoising autoencoder, with taking account of heterogeneous side information. The DLMR provides a flexible scheme to leverage the available resources for recommendation, which is able to explore user's latent interests, capture the intricate interactions between users and items, and provide accurate and personalized recommendations. Experimental analysis over real world data sets demonstrates that DLMR could provide high performance top-N recommendation in sparse settings and outperform state-of-the-art recommender approaches significantly.

关键词： Sparse latent Dirichlet allocation interest exploring convolutional neural network denoising autoencoder top-N recommendation

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