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  • Open Access


    Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

    Yatian Shen1, Yubo Mai2, Xiajiong Shen2, Wenke Ding2, *, Mengjiao Guo3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 529-541, 2020, DOI:10.32604/cmc.2020.011139

    Abstract Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer in the training process of… More >

  • Open Access


    Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001

    Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we… More >

  • Open Access


    Image Information Hiding Method Based on Image Compression and Deep Neural Network

    Xintao Duan1, *, Daidou Guo1, Chuan Qin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 721-745, 2020, DOI:10.32604/cmes.2020.09463

    Abstract Image steganography is a technique that hides secret information into the cover image to protect information security. The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image, which will cause the size of the secret image to be much smaller than the cover image. Therefore, the problem of small steganographic capacity needs to be solved urgently. This paper proposes a steganography framework that combines image compression. In this framework, the Vector Quantized Variational AutoEncoder (VQ-VAE) is used to achieve the compression of the secret image. The… More >

  • Open Access


    Data Driven Modelling of Coronavirus Spread in Spain

    G. N. Baltas1, *, F. A. Prieto1, M. Frantzi2, C. R. Garcia-Alonso1, P. Rodriguez1, 3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1343-1357, 2020, DOI:10.32604/cmc.2020.011243

    Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of these measures, depends on the… More >

  • Open Access


    Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network

    Xiaoyan Zhao1, *, Shuwen Chen2, Lin Zhou3, Ying Chen3, 4

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 253-271, 2020, DOI:10.32604/cmc.2020.09848

    Abstract Microphone array-based sound source localization (SSL) is a challenging task in adverse acoustic scenarios. To address this, a novel SSL algorithm based on deep neural network (DNN) using steered response power-phase transform (SRP-PHAT) spatial spectrum as input feature is presented in this paper. Since the SRP-PHAT spatial power spectrum contains spatial location information, it is adopted as the input feature for sound source localization. DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features. SRP-PHAT at each steering position within a frame is arranged into a vector, which… More >

  • Open Access


    MII: A Novel Text Classification Model Combining Deep Active Learning with BERT

    Anman Zhang1, Bohan Li1, 2, 3, *, Wenhuan Wang1, Shuo Wan1, Weitong Chen4

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1499-1514, 2020, DOI:10.32604/cmc.2020.09962

    Abstract Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rare work paid attention to the effectiveness of active learning on it. In this paper, we proposed a deep active learning model with bidirectional encoder representations from transformers (BERT) for text classification. BERT takes advantage of the self-attention mechanism to integrate contextual information, which is beneficial to accelerate the convergence of training. As for the process of active learning, we design an instance selection strategy based on… More >

  • Open Access


    Left or Right Hand Classification from Fingerprint Images Using a Deep Neural Network

    Junseob Kim1, Beanbonyka Rim1, Nak-Jun Sung1, Min Hong2, *

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 17-30, 2020, DOI:10.32604/cmc.2020.09044

    Abstract Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individual’s lifetime and is a highly reliable and secure way to identify a certain individuals. AFIS (Automated Fingerprint Identification System) is a system used by Korean police for identifying a specific person by fingerprint. The AFIS system, however, only selects a list of possible candidates through fingerprints, the exact individual must be found by fingerprint experts. In this paper, we designed a deep learning system using deep convolution network to categorize fingerprints as coming from… More >

  • Open Access


    Defend Against Adversarial Samples by Using Perceptual Hash

    Changrui Liu1, Dengpan Ye1, *, Yueyun Shang2, Shunzhi Jiang1, Shiyu Li1, Yuan Mei1, Liqiang Wang3

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1365-1386, 2020, DOI:10.32604/cmc.2020.07421

    Abstract Image classifiers that based on Deep Neural Networks (DNNs) have been proved to be easily fooled by well-designed perturbations. Previous defense methods have the limitations of requiring expensive computation or reducing the accuracy of the image classifiers. In this paper, we propose a novel defense method which based on perceptual hash. Our main goal is to destroy the process of perturbations generation by comparing the similarities of images thus achieve the purpose of defense. To verify our idea, we defended against two main attack methods (a white-box attack and a black-box attack) in different DNN-based image classifiers and show that,… More >

  • Open Access


    Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis

    Kemal Akyol1, *

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 619-632, 2020, DOI:10.32604/cmes.2020.07632

    Abstract Parkinson’s disease is a serious disease that causes death. Recently, a new dataset has been introduced on this disease. The aim of this study is to improve the predictive performance of the model designed for Parkinson’s disease diagnosis. By and large, original DNN models were designed by using specific or random number of neurons and layers. This study analyzed the effects of parameters, i.e., neuron number and activation function on the model performance based on growing and pruning approach. In other words, this study addressed the optimum hidden layer and neuron numbers and ideal activation and optimization functions in order… More >

  • Open Access


    Parameters Compressing in Deep Learning

    Shiming He1, Zhuozhou Li1, Yangning Tang1, Zhuofan Liao1, Feng Li1, *, Se-Jung Lim2

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.06130

    Abstract With the popularity of deep learning tools in image decomposition and natural language processing, how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved. These parameters are huge and can be as many as millions. At present, a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure. These methods include matrix decomposition and tensor decomposition. To let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,… More >

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