基于距离与图卷积网络的方面级情感分析

目前,基于卷积神经网络和循环神经网络的方面级情感分析研究工作较少同时考虑到句子的句法结构和词语的语法距离,且卷积神经网络和循环神经网络无法有效地处理图结构的数据.针对上述问题,提出了一种基于距离与图卷积网络的方面级情感分类模型.首先,为该模型设计了一个具有残差连接的双层双向长短期记忆网络,用于提取句子的上下文信息;然后,根据句法依赖树得到词语的语法距离权重,并根据词语之间的句法关系构建邻接矩阵;最后,采用图卷积网络结合句子的上下文信息、语法距离权重和邻接矩阵提取方面的情感特征.实验结果表明,模型是有效的且可获得更好的性能.     

基于深度学习的海岸线边缘检测网络模型

海岸线的动态监测对海岸带的规划管理具有非常重要的意义。由于海陆环境错综复杂,遥感影像中海陆边界光谱特征不明显,导致提取的海岸线定位不准确。提出一种融合语义分割网络和边缘检测网络的深度卷积神经网络模型(EWNet)。该模型包含2个分支流：语义分割流负责提取分层语义信息并用来指导边缘检测流获取岸线语义信息;边缘检测流通过语义分割流完善边缘语义信息。在“高分一号”遥感图像上的实验结果表明,与几种最新网络模型相比,EWNet获得了更精确的海岸线边界提取结果。     

A comprehensive survey on deep learning based malware detection techniques

Recent theoretical and practical studies have revealed that malware is one of the most harmful threats to the digital world. Malware mitigation techniques have evolved over the years to ensure security. Earlier, several classical methods were used for detecting malware embedded with various features like the signature, heuristic, and others. Traditional malware detection techniques were unable to defeat new generations of malware and their sophisticated obfuscation tactics. Deep Learning is increasingly used in malware detection as DL-based systems outperform conventional malware detection approaches at finding new malware variants. Furthermore, DL-based techniques provide rapid malware prediction with excellent detection rates and analysis of different malware types. Investigating recently proposed Deep Learning-based malware detection systems and their evolution is hence of interest to this work. It offers a thorough analysis of the recently developed DL-based malware detection techniques. Furthermore, current trending malwares are studied and detection techniques of Mobile malware (both Android and iOS), Windows malware, IoT malware, Advanced Persistent Threats (APTs), and Ransomware are precisely reviewed.     

基于深度学习的实例分割研究综述

深度学习在计算机视觉领域已经取得很大发展,虽然基于深度学习的实例分割研究近年来才成为研究热点,但其技术可广泛应用在自动驾驶,辅助医疗和遥感影像等领域。实例分割作为计算机视觉的基础问题之一,不仅需要对不同类别目标进行像素级别分割,还要对不同目标进行区分。此外,目标形状的灵活性,不同目标间的遮挡和繁琐的数据标注问题都使实例分割任务面临极大的挑战。本文对实例分割中一些具有价值的研究成果按照两阶段和单阶段两部分进行了系统性的总结,分析了不同算法的优缺点并对比了模型在COCO数据集上的测试性能,归纳了实例分割在特殊条件下的应用,简要介绍了常用数据集和评价指标。最后,对实例分割未来可能的发展方向及其面临的挑战进行了展望。     

Effective deep learning based multimodal sentiment analysis from unstructured big data

More recently, as images, memes and graphics interchange formats have dominated social feeds, typographic/infographic visual content has emerged as an important social media component. This multimodal text combines text and image, defining a novel visual language that must be analysed because it has the potential to modify, confirm or grade the sentiment's polarity. The problem is how to effectively use information from the visual and textual content in image-text posts. This article presents a new deep learning-based multimodal sentiment analysis (MSA) model using multimodal data such as images, text and multimodal text (image with embedded text). The text analytic unit, the discretization control unit, the picture analytic component and the decision-making component are all included in this system. The discretization unit separates the text from the picture using the variant and channel augmented maximally stable extremal regions (VCA-MSERs) technique, which are then analysed as discrete elements and fed into the appropriate image and text analytics units. The text analytics system utilizes a stacked recurrent neural network with multilevel attention and feedback module (SRNN-MAFM) to detect the sentiment of the text. A deep convolutional neural network (CNN) structure with parallel-dilated convolution and self-attention module (PDC-SAM) is developed to forecast the emotional response to visual content. Finally, the decision component employs a Boolean framework including an OR function to evaluate and classify the output into three fine-grained sentiment classes: positive, neutral and negative. The proposed work is simulated in the python platform using the STS-Gold, Flickr 8k and B-T4SA datasets for sentiment analysis of text and visual and multimodal text. Simulation outcomes proved that the suggested method achieved better accuracy of 97.8%, 97.7% and 90% for text, visual and MSA individually compared to other methods.     

Aspect category detection using statistical and semantic association

Aspect category detection (ACD) is an important subtask of aspect-based sentiment analysis (ABSA). It is a challenging problem due to subjectivity involved in categorization, as well as the existence of overlapping classes. Among various approaches that have been applied to ACD include rule-based approaches along with other machine learning approaches, and most of them are statistical in nature. In this article, we have used an association rule-based approach. To deal with the statistical limitation of association rules, we proposed a hybridized rule-based approach that combines association rules with the semantic association. For semantic associations, we have used the notion of word-embeddings. Experiments were performed on SemEval dataset, a standard benchmark dataset for aspect categorization in the restaurant domain. We observed that semantic associations can complement statistical association and improve the accuracy of classification. The proposed method performs better than several state-of-the-art methods.     

基于深度学习的单幅图像超分辨率重建综述

单幅图像超分辨率SISR重建指从单幅低分辨率图像恢复出高分辨率图像.深度学习方法越来越多地用于图像超分辨重建领域,由于深度网络模型可以自主学习低分辨率图像到高分辨率图像之间的映射关系,与传统方法相比在该领域展现出了更好的重建效果,因而基于深度学习的方法已经成为目前图像超分辨率重建领域的主流方向.围绕现有的超分辨深度网络...     

An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features

Accurate protein secondary structure prediction (PSSP) is essential to identify structural classes, protein folds, and its tertiary structure. To identify the secondary structure, experimental methods exhibit higher precision with the trade-off of high cost and time. In this study, we propose an effective prediction model which consists of hybrid features of 42-dimensions with the combination of convolutional neural network (CNN) and bidirectional recurrent neural network (BRNN). The proposed model is accessed on four benchmark datasets such as CB6133, CB513, CASP10, and CAP11 using Q3, Q8, and segment overlap (Sov) metrics. The proposed model reported Q3 accuracy of 85.4%, 85.4%, 83.7%, 81.5%, and Q8 accuracy 75.8%, 73.5%, 72.2%, and 70% on CB6133, CB513, CASP10, and CAP11 datasets respectively. The results of the proposed model are improved by a minimum factor of 2.5% and 2.1% in Q3 and Q8 accuracy respectively, as compared to the popular existing models on CB513 dataset. Further, the quality of the Q3 results is validated by structural class prediction and compared with PSI-PRED. The experiment showed that the quality of the Q3 results of the proposed model is higher than that of PSI-PRED.     

Data-driven soft sensor development based on deep learning technique

In industrial process control, some product qualities and key variables are always difficult to measure online due to technical or economic limitations. As an effective solution, data-driven soft sensors provide stable and reliable online estimation of these variables based on historical measurements of easy-to-measure process variables. Deep learning, as a novel training strategy for deep neural networks, has recently become a popular data-driven approach in the area of machine learning. In the present study, the deep learning technique is employed to build soft sensors and applied to an industrial case to estimate the heavy diesel 95% cut point of a crude distillation unit (CDU). The comparison of modeling results demonstrates that the deep learning technique is especially suitable for soft sensor modeling because of the following advantages over traditional methods. First, with a complex multi-layer structure, the deep neural network is able to contain richer information and yield improved representation ability compared with traditional data-driven models. Second, deep neural networks are established as latent variable models that help to describe highly correlated process variables. Third, the deep learning is semi-supervised so that all available process data can be utilized. Fourth, the deep learning technique is particularly efficient dealing with massive data in practice.     

Spindle thermal error prediction approach based on thermal infrared images: A deep learning method

It is essential to precisely model the spindle thermal error due to its dramatic influence on the machining accuracy. In this paper, the deep learning convolutional neural network (CNN) is used to model the axial and radial thermal errors of horizontal and vertical spindles. Unlike the traditional CNN model that relies entirely on thermal images, this model combines the thermal image with the thermocouple data to fully reflect the temperature field of the spindle. After pre-processing and data enhancement of the thermal images, a multi-classification model based on CNN is built and verified for accuracy and robustness. The experimental results show that the model prediction accuracy is approximately 90 %–93 %, which is higher than the BP model. When the spindle rotation speed changes, the model also shows good robustness. Real cutting tests show that the deep learning model has good applicability to the spindle thermal error prediction and compensation.     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

基于交互特征表示的评价对象抽取模型

评价对象抽取是对象级情感分析的关键任务之一,评价对象抽取结果会直接影响对象级情感分类的准确率.在评价对象抽取任务中,借助手工特征加强模型性能的方式既消耗时间又耗费人力.针对数据规模小、特征信息不充分等问题,提出一种基于交互特征表示的评价对象抽取模型(aspect extraction model based on interactive feature representation, AEMIFR).相比其他模型,AEMIFR模型结合字符级嵌入与单词嵌入,捕获单词的语义特征、字符的形态特征以及字符与词语之间的内在联系.而且,AEMIFR模型获取文本的局部特征表示和上下文依赖特征表示,并学习2种特征表示之间的交互关系,增强2种特征之间的相似特征的重要性,减少无用特征对模型的消极影响,以及学习更高质量的特征表示.最后在SemEval 2014,SemEval 2015,SemEval 2016中的数据集L-14,R-14,R-15,R-16上进行实验,取得具有竞争力的效果.     

Exploration in deep reinforcement learning: A survey

This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challenging for reinforcement learning to learn rewards and actions association. Thus more sophisticated exploration methods need to be devised. This review provides a comprehensive overview of existing exploration approaches, which are categorised based on the key contributions as: reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based methods, safe exploration and random-based methods. Then, unsolved challenges are discussed to provide valuable future research directions. Finally, the approaches of different categories are compared in terms of complexity, computational effort and overall performance.     

Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources

Detecting road damage quickly and accurately facilitates the ability of road-maintenance agencies to make timely repairs to road surfaces, maintain optimal road conditions, optimize transportation safety, and minimize transportation costs. An extensive evaluation of eight deep-learning-based road-damage detection models was conducted in this study. Each model was trained on 9493 images sourced from multiple databases. The 16165 instances of road damage in these images were categorized into five types of damage, including longitudinal crack, horizontal crack, alligator damage, pothole-related crack, and line blurring. Two experiments were conducted that identified two models, single shot multi-box detector (SSD) Inception V2 and faster region-based convolutional neural networks (R-CNN) Inception V2, as providing the best balance of road-damage-detection accuracy and image processing time. These experiments demonstrated that increasing the diversity of image sources improved road-damage-detection model performance. In addition to combining data images from different sources with consistently relabeled damage instances, this study released road-damage image data from the road maintenance agency in Zhubei, Hsinchu County, Taiwan for research and other uses, increasing the limited amount of published image data sources and positively impacting future scholarly research into road damage detection.     

Addressing image and Poisson noise deconvolution problem using deep learning approaches

Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise, is caused in the images due to the statistical essence of electromagnetic waves. X-ray, visible light, and gamma rays are electromagnetic waves. The enhancement of the convolution model in addressing images is challenging due to the various constituents such as optical aberrations, noise level, and optical setup. The modeling configuration of the image is attained using the point spread function (PSF), which is responsible for the system's impulse response. The quality image is retrieved by denoising and super-resolution (SR) methods; these methods simultaneously eliminate the noise content from the images. A Richardson–Lucy and alternating direction method of multipliers type of non-blind iterative algorithmic approaches associated with the PSF performance in addressing image is comparatively analyzed. The deep learning approach, convolutional neural networks (CNNs), is also employed to understand the nonlinear mapping relationship between the observed data and ground reality. The performance of the various network approaches is compared in this article. The result obtained shows that the deep learning CNNs achieved higher accuracy in producing denoising images. The goal of the proposed system model is to remove the interference noise in images. The high-resolution images are obtained by implementing a SR-based CNN model.     

基于频域叠加和深度学习的频谱信号识别

针对频谱监测领域频带较宽、接收机采样步进受限而造成的宽带频谱信号难以识别的问题,提出了一种频域叠加预处理和目标检测相结合的频谱信号识别方法。该方法利用频域叠加对频谱数据进行多帧叠加来突出频谱中的弱信号,将叠加处理后的频谱图像送入改进的目标检测网络中进行信号类型的识别。经过实验证明,此方法能够有效地识别7种类型的频谱信号,提出的频域叠加预处理能够提升目标检测算法的精度,并提高频谱中弱信号的识别能力,在信噪比为6 dB时,算法能够达到89.7%的平均识别率。     

深度学习行人检测方法综述

行人检测技术在智能交通系统、智能安防监控和智能机器人等领域均表现出了极高的应用价值,已经成为计算机视觉领域的重要研究方向之一。得益于深度学习的飞速发展,基于深度卷积神经网络的通用目标检测模型不断拓展应用到行人检测领域,并取得了良好的性能。但是由于行人目标内在的特殊性和复杂性,特别是考虑到复杂场景下的行人遮挡和尺度变化等问题,基于深度学习的行人检测方法也面临着精度及效率的严峻挑战。本文针对上述问题,以基于深度学习的行人检测技术为研究对象,在充分调研文献的基础上,分别从基于锚点框、基于无锚点框以及通用技术改进(例如损失函数改进、非极大值抑制方法等)3个角度,对行人检测算法进行详细划分,并针对性地选取具有代表性的方法进行详细结合和对比分析。本文总结了当前行人检测领域的通用数据集,从数据构成角度分析各数据集应用场景。同时讨论了各类算法在不同数据集上的性能表现,对比分析各算法在不同数据集中的优劣。最后,对行人检测中待解决的问题与未来的研究方法做出预测和展望。如何缓解遮挡导致的特征缺失问题、如何应对单一视角下尺度变化问题、如何提高检测器效率以及如何有效利用多模态信息提高行人检测精度,均是值得进一步...     

A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis

Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in practical engineering, the fault modes of rolling bearings are usually compound faults and contain a large amount of noise, which increases the difficulty of fault diagnosis. Therefore, a deep feature enhanced reinforcement learning method is proposed for the fault diagnosis of rolling bearing. Firstly, to improve robustness, the neural network is modified by the Elu activation function. Secondly, attention model is used to improve the feature enhanced ability and acquire essential global information. Finally, deep Q network is established to accurately diagnosis the fault modes. Sufficient experiments are conducted on the rolling bearing dataset. Test result shows that the proposed method is superior to other intelligent diagnosis methods.     

基于深度学习的视觉SLAM回环检测方法

现今主要的视觉SLAM回环检测方法是基于人工标记特征点算法进行图像间匹配,在复杂环境下会出现准确率急速下降的问题。针对此问题,结合卷积神经网络和局部敏感哈希算法,提出一种基于深度学习的回环检测方法。基于回环检测中的图像相似性判断策略构建图像特征向量集,运用级联的余弦距离哈希函数进行回环检测。实验结果表明,该方法较传统方法有着更高的准确率与速率,更好满足了视觉SLAM系统对消除累计误差和实时性的要求。