Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data (...) utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. (shrink)

How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network is established to do classification. It has been shown that the MIP-based method can reduce the (...) demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close. (shrink)

Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional neural networks (CNNs) are the advanced version of DNNs that achieve 4–12% relative gain in the word error rate (WER) over DNNs. Existence of spectral variations and local correlations in speech signal makes CNNs more capable of speech recognition. Recently, it has been demonstrated that bidirectional long short-term memory (BLSTM) produces higher recognition rate in acoustic modeling because they are adequate to reinforce higher-level representations of acoustic (...) data. Spatial and temporal properties of the speech signal are essential for high recognition rate, so the concept of combining two different networks came into mind. In this paper, a hybrid architecture of CNN-BLSTM is proposed to appropriately use these properties and to improve the continuous speech recognition task. Further, we explore different methods like weight sharing, the appropriate number of hidden units, and ideal pooling strategy for CNN to achieve a high recognition rate. Specifically, the focus is also on how many BLSTM layers are effective. This paper also attempts to overcome another shortcoming of CNN, i.e. speaker-adapted features, which are not possible to be directly modeled in CNN. Next, various non-linearities with or without dropout are analyzed for speech tasks. Experiments indicate that proposed hybrid architecture with speaker-adapted features and maxout non-linearity with dropout idea shows 5.8% and 10% relative decrease in WER over the CNN and DNN systems, respectively. (shrink)

In recent debates in moral psychology and literary Darwinism, several authors suggested that narrative art plays a significant role in the process of the social learning of moral norms, functioning as storage of locally salient moral information. However, an integrative view, which would help explain the inner workings of this morally educative function of narrative art, is still lacking. This paper provides such a unifying theoretical account by bringing together insights from moral psychology, educational sciences, cognitive/evolutionary narratology, and cultural evolution. (...) A study of contemporary American rap music is included to showcase the benefits of this integrative approach in the context of one particular type of narrative art. (shrink)

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. (...) This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides. (shrink)

The most predominant kind of disease that is normal among ladies is breast cancer. It is one of the significant reasons among ladies, regardless of huge endeavors to stay away from it through screening developers. An automatic detection system for disease helps doctors to identify and provide accurate results, thereby minimizing the death rate. Computer-aided diagnosis has minimum intervention of humans and produces more accurate results than humans. It will be a difficult and long task that depends on the expertise (...) of pathologists. Deep learning methods proved to give better outcomes when correlated with ML and extricate the best highlights of the images. The main objective of this paper is to propose a deep learning technique in combination with a convolution neural network and long short-term memory with a random forest algorithm to diagnose breast cancer. Here, CNN is used for feature extraction, and LSTM is used for extracted feature detection. The experimental results show that the proposed system accomplishes 100% of accuracy, a sensitivity of 99%, recall of 99%, and an F1-score of 98% compared to other traditional models. As the system achieved correct results, it can help doctors to investigate breast cancer easily. (shrink)

Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work, computational results are presented for a modified curvature sensor, the tomographic pupil image wavefront sensor, which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional neural networks are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely (...) known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements. The reconstructed wavefronts from both techniques are compared for wavefronts of 153 Zernike modes. For this case, a detailed comparison and grid search to find the most suitable neural network is performed, searching between multi-layer perceptron, CNN and recurrent networks topologies. In general, the best network was a CNN trained for TPI-WFS reconstruction, achieving better performance than the reconstruction software from TPI-WFS in most of the turbulent profiles, but the most significant improvements were found for higher turbulent profiles that have the lowest r0 values. (shrink)

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks. However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of (...) three facial expressions. To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp. (shrink)

Media use by ethnic minorities is increasingly becoming a politicized matter, appearing regularly in discussions on multiculturalism and integration. In a globalizing media-landscape, the rise of ethnic and global ethnic media paradoxically enables ethnic minorities to maintain links with forms of ethnic identity. Especially interesting in this respect is media use by adolescents, often on the crossroads between different cultures. This article departs from the notion concerned with the extent to which media use of adolescents from ethnic minorities actually differs (...) from that of their Belgian counterparts, and instead focuses on the role ethnicity plays as a determinant of media use when weighed up against other socio-economic or education-related variables. Based on a large-scale Belgian survey of 12- to 18-year-olds, this article shows, amongst other results, that the importance of ethnic-cultural background as an indicator for media use by adolescents from ethnic minorities ought to be placed into perspective. (shrink)

The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in order to (1) (...) achieve concepts, and (2) represent objects, as opposed to “half-entities” corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called “approximate meaning[s]” (Marcus & Davis, Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the “complete abandonment of the empiricist theory of abstraction” (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine’s “sortal predicates, negation, plurals, identity, pronouns, and quantifiers” which are representationally necessary to overcome the regress/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi’s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf’s proposal (“Observational Concepts.” The Conceptual Mind. MIT, 2015. 223–248). (shrink)

Artificial intelligence techniques in general and convolutional neural networks in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality (...) issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set. (shrink)

A lot of effort is currently made to provide methods to analyze and understand deep neural network impressive performances for tasks such as image or text classification. These methods are mainly based on visualizing the important input features taken into account by the network to build a decision. However these techniques, let us cite LIME, SHAP, Grad-CAM, or TDS, require extra effort to interpret the visualization with respect to expert knowledge. In this paper, we propose a novel approach to inspect (...) the hidden layers of a fitted CNN in order to extract interpretable linguistic objects from texts exploiting classification process. In particular, we detail a weighted extension of the Text Deconvolution Saliency (wTDS) measure which can be used to highlight the relevant features used by the CNN to perform the classification task. We empirically demonstrate the efficiency of our approach on corpora from two different languages: English and French. On all datasets, wTDS automatically encodes complex linguistic objects based on co-occurrences and possibly on grammatical and syntax analysis. (shrink)

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory improved on bidirectional long short-term memory adds (...) 1 − tanh function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network, and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron, recurrent neural network, long short-term memory, BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error, root-mean-squared error, and R-square evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks. (shrink)

The article explores two different articulations of the attitudes of young Chinese netizens towards the state: the neo-nationalist web community, ‘Anti-CNN.com’/‘April Youth’, and the online ‘machinema’ film, ‘Online Gaming Addicts’ War’. Both of these online practices are associated with the post-’80 s generation, which I argue is a key constituency in contemporary Chinese internet discourse. Through these case studies, the article explores the viability of recent attempts to apply Foucauldian theories of governmentality to the case of China. It identifies a (...) determining factor here in the recurring tendency of the Party-state, whilst generally attempting to embrace more sophisticated forms of governance, to default towards crude techniques and technologies of prohibition in its regulatory stance towards the internet. Whilst this stance is likely to be unsustainable in view of the dynamics of global media culture, the article argues that this does not undermine claims over the existence of a distinctive Chinese form of governmentality. Rather, the Chinese case should strengthen doubts over the view of some that governmentality is incompatible with the state’s deployment of violence. (shrink)

Rheumatoid arthritis is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manual method is time-consuming and laborious. Even if two radiologists evaluate a specific location, their subjective evaluation may lead to low inter-rater reliability. Here, we developed an efficient model powered by deep convolutional neural networks to solve these problems (...) and automated the overall scoring on hand X-rays. The depthwise separable convolution technique is used based on ResNet-50 due to the high resolution of hand X-rays. An inverted residual block is introduced to devise a ResNet-Dwise50 model to enhance the efficiency of the model. The model was trained and tested using bilateral posteroanterior images of 3818 patients. The experiment results show the ResNet-Dwise50 model achieved an MAE of 14.90 and RMSE of 22.01 while ensuring high efficiency. There was no statistical difference between the average scores given by two experienced radiologists and predicted scores from our model. (shrink)

This paper examines how the slogan of the “Chinese Dream” is represented in two western news reports on the CNN and the BBC websites. They are among the first news reports which introduce the “Chinese Dream” into the US and the UK, respectively. The analysis of both the verbal news texts and the visuals shows that the reporters use different discursive strategies to manipulate the ideological orientation of the social actors and social actions in discourse. Through the analysis, this study (...) shows how different discursive resources conceptualized in Theo van Leeuwen’s work, Discourse and practice: New tools for critical discourse analysis, are used to stereotype “the other’s” politics and political discourse in BBC and CNN’s news texts, and perpetuate a Eurocentric view on perceiving contemporary Chinese political discourse. (shrink)

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve (...) the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory. (shrink)

Aiming at the problem of radar target recognition of High-Resolution Range Profile under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network, Short-time Fourier Transform, and Convolutional Neural Network is proposed. Combining the Least-Squares Generative Adversarial Network with the Wasserstein Generative Adversarial Network with Gradient Penalty, the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning (...) and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory network method. (shrink)

Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network (...) model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms. (shrink)

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing (...) such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively. (shrink)

Ship-type identification in a maritime context can be critical to the authorities to control the activities being carried out. Although Automatic Identification Systems has been mandatory for certain vessels, if a vessel does not have them voluntarily or not, it can lead to a whole set of problems, which is why the use of tracking alternatives such as radar is fully complementary for a vessel monitoring systems. However, radars provide positions, but not what they are detecting. Having systems capable of (...) adding categorical information to radar detections of vessels makes it possible to increase control of the activities being carried out, improve safety in maritime traffic, and optimize on-site inspection resources on the part of the authorities. This paper addresses the binary classification problem (fishing ships versus all other vessels) using unbalanced data from real vessel trajectories. It is performed from a deep learning approach comparing two of the main trends, Convolutional Neural Networks and Long Short-Term Memory. In this paper, it is proposed the weighted cross-entropy methodology and compared with classical data balancing strategies. Both networks show high performance when applying weighted cross-entropy compared with the classical machine learning approaches and classical balancing techniques. This work is shown to be a novel approach to the international problem of identifying fishing ships without context. (shrink)

Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks to aid in the detection of cardiac arrhythmias. The data collected by (...) an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat, left bundle branch block beat, right bundle branch block beat, and premature ventricular contraction. The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems. (shrink)

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) (...) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations. (shrink)

During the Gulf war, CNN correspondent Peter Arnett distinguished himself with its courageous reporting in Iraq while under fire by the U.S.-led coalition which dropped more bombs on Iraq than were unleashed in World War II. Reporting live from Baghdad throughout the war, Arnett provided vivid daily accounts of life in Iraq during one of the most sustained air attacks in history. From his live telephone reporting of the early hours of the U.S. attack on Iraq in January 1991 through (...) his live satellite reports of the effects of the daily bombing of Iraq, Arnett distinguished himself through his attempts to cut through the lies and disinformation of both sides and to provide accurate reporting on the effects of the U.S.-led coalition assault against Iraq. (shrink)

Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling processes (...) is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR’s path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition. (shrink)

The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education. Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction system. Secondly, the CAI model is implemented based on the weight sharing and local perception of the Convolutional Neural Network. Finally, the performance of the CNN-based CAI model is tested. Meanwhile, it analyses students’ IEE experience under the proposed CAI model through a case study of (...) Music Majors from Xi’an Conservatory of Music. The experimental data show that the CNN-based CAI model can respond quickly and stably when users access different functional modules, such as webpage browsing. The proposed CAI model increases students’ entrepreneurial interest, skills, and knowledge by 55.62, 57.32, and 72.12%, respectively. Students’ entrepreneurial practice ability has been improved by over 50.00%; such an increase in entrepreneurial practice ability has also shown individual differences. Thus, the proposed Music Majors-oriented IEE-infiltrated CAI model based on CNN improves students’ entrepreneurial practice ability and reflects the positive experience of Music Majors on IEE. The finding provides references for the step-by-step identification of the CNN-based CAI model and has certain guiding significance for analyzing the effect of college IEE. (shrink)

Timely detection and efficient recognition of fault are challenging for the bogie of high-speed train, owing to the fact that different types of fault signals have similar characteristics in the same frequency range. Notice that convolutional neural networks are powerful in extracting high-level local features and that recurrent neural networks are capable of learning long-term context dependencies in vibration signals. In this paper, by combining CNN and RNN, a so-called convolutional recurrent neural network is proposed to diagnose various faults of (...) the HST bogie, where the capabilities of CNN and RNN are inherited simultaneously. Within the novel architecture, the proposed CRNN first filters out the features from the original data through convolutional layers. Then, four recurrent layers with simple recurrent cell are used to model the context information in the extracted features. By comparing the performance of the presented CRNN with CNN, RNN, and ensemble learning, experimental results show that CRNN achieves not only the best performance with accuracy of 97.8% but also the least time spent in training model. (shrink)

After hearing Donald Trump's acceptance speech at the Republican National Convention held in Cleveland, Ohio, Newt Gingrich was interviewed live on CNN about the menacing tone of the address. Gingrich not only defended Trump's nearly apocalyptic vision of America if he was not elected, the former Speaker of the House swiped aside the clear data that indicated that the criminalized landscapes portrayed in Trump's speech might just be the work of a frenzied and fearful imagination rather than based in fact. (...) "'People feel more threatened,'" Gingrich insisted. "'As a political candidate, I'll go with what people feel '". Commenting in Forbes shortly thereafter, Ethan Siegel, a scientist and science... (shrink)

This paper investigates the stochastically exponential stability of reaction-diffusion impulsive stochastic cellular neural networks. The reaction-diffusion pulse stochastic system model characterizes the complexity of practical engineering and brings about mathematical difficulties, too. However, the difficulties have been overcome by constructing a new contraction mapping and an appropriate distance on a product space which is guaranteed to be a complete space. This is the first time to employ the fixed point theorem to derive the stability criterion of reaction-diffusion impulsive stochastic CNN (...) with distributed time delays. Finally, an example is provided to illustrate the effectiveness of the proposed methods. (shrink)

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant (...) to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: (i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; (ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and (iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task. (shrink)

The 1991 war against Iraq was one of the first televised events of the global village in which the entire world watched a military spectacle unfold via global TV satellite networks.1 In retrospect, the Bush administration and the Pentagon carried out one of the most successful public relations campaigns in the history of modern politics in its use of the media to mobilize support for the war. The mainstream media in the United States and elsewhere tended to be a compliant (...) vehicle for the government strategy to manipulate the public, imperiling democracy which requires informed citizens, checks and balances against excessive government power, and a free and vigorous critical media (see Kellner 1990 and 1992). Indeed, if the media do not adequately inform citizens, provide a check against excessive government power and corruption, and adequately debate the key issues of the day, democracy is undermined. M oreover, the U.S. media, especially CNN, completely dominated global coverage of the event. CNN had cameras and reporters in Baghdad throughout the war, a large crew in Israel, and live coverage of all U.S. military and government press conferences. Thus its images, discourses, and material tended to shape global coverage of the event. This meant that the Bush administration and Pentagon was able to control the flow of images and discourses and thus to manage the TV spectacle of the Gulf War.2 In this chapter, I first discuss the production of the text of the "crisis in the Gulf" and then "the Gulf war." This will involve analysis of disinformation and propaganda campaigns by the Bush administration, the Pentagon, and their allies, as well as dissection of the constraints produced by the so-called pool system. I also indicate how the political economy of the media in the United States facilitated the manufacturing of consent for U.S. government policies. Then I analyze the meanings embedded in the text of the war against Iraq and the reception of the text/event by the audience.. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...) belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Ice Cube "What's a brother gotta do to get a message through to the Red, White, and Blue?" Ice-T Rap music has emerged as one of the most distinctive and controversial music genres of the past decade. A significant part of hip hop culture, [1] rap articulates the experiences and conditions of African-Americans living in a spectrum of marginalized situations ranging from racial stereotyping and stigmatizing to struggle for survival in violent ghetto conditions. In this cultural context, rap provides a (...) voice to the voiceless, a form of protest to the oppressed, and a mode of alternative cultural style and identity to the marginalized. Rap is thus not only music to dance and party to, but a potent form of cultural identity. It has become a powerful vehicle for cultural political expression, serving as the "CNN of black people", or upping the high-tech ante, as their "satellite communication system". It is an informational medium to tune into, one that describes the rage of African-Americans facing growing oppression, declining opportunities for advancement, changing moods on the streets, and everyday life as a matter of sheer survival. In turn, it has become a cultural virus, circulating its images, sounds, and attitude throughout the culture and body politic. (shrink)

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

This research aims to analyze the influencing factors of migrant children’s education integration based on the convolutional neural network algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set, Stanford sentiment data set, and news opinion data set are used to analyze the classification accuracy, loss value, Hamming loss, precision, recall, and micro-F1 of the ALGCNN model. Then, on the big data platform, (...) data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest, the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children. The average final score of local children was greatly higher than that of migrant children, whereas the scores of migrant children’s listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model is established based on the CNN algorithm. In short, the children’s education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children’s educational integration, and this work provides a reference for the analysis of migrant children’s educational integration. (shrink)

During the Gulf war, CNN correspondent Peter Arnett distinguished himself with its courageous reporting in Iraq while under fire by the U.S.-led coalition which dropped more bombs on Iraq than were unleashed in World War II. Reporting live from Baghdad throughout the war, Arnett provided vivid daily accounts of life in Iraq during one of the most sustained air attacks in history. From his live telephone reporting of the early hours of the U.S. attack on Iraq in January 1991 through (...) his live satellite reports of the effects of the daily bombing of Iraq, Arnett distinguished himself through his attempts to cut through the lies and disinformation of both sides and to provide accurate reporting on the effects of the U.S.-led coalition assault against Iraq. (shrink)