Reviewerly/exHarmony huggingface dataset:
Seismic Fault Identification Using Graph High-Frequency Components as Input to Graph Convolutional Network

Many activities such as drilling and exploration in the oil and gas industries rely on identifying seismic faults. Using graph high-frequency components as inputs to a graph convolutional network, we propose a method for detecting faults in seismic data. In Graph Signal Processing (GSP), digital signal processing (DSP) concepts are mapped to define the processing techniques for signals on graphs. As a first step, we extract patches of the seismic data centered around the points of concern. Each patch is then represented in a graph domain, with the seismic amplitudes as the graph signals. We attenuate the low-frequency components of the signal with the aid of a graph high-pass filter. By applying the graph Fourier transform, we obtain the graph high-frequency components. These graph high-frequency components act as inputs to a graph convolutional network (GCN). By classifying the patches using GCN, we identify the faults in data.
Pressure Sensor Data Modeling with Recurrent Conditional Generative Adversarial Networks

Several critical systems in an autonomous car strongly rely on pressure sensors to track and measure critical specifications, which has winded up to be the crucial aspect in making secure roadways, enhancing the driving experience, and minimizing pollution. Autonomous car driving experience would be completely different without all types of pressure sensors employed in the contemporary car, these sensors aid to handle braking systems to auto electrical windows, exhaustive emissions to the power steering. Monitoring the health of these pressure sensors is done by physically collecting the time series data at different scenarios. This approach is expensive and time-consuming. As a result, autonomous vehicle corporations are focusing on virtual authentication where synthetic data is generated for several scenarios. RCGAN is exploited in this work, for generating time series synthetic sensor data. This algorithm employs RNN, which trains the network model on supplementary information. This change permits RCGAN to learn and create sensible, time-series sensor data.
A Smart Framework to Analyze Hotel Services after COVID-19

In the last decade, many websites have allowed customers to provide reviews about their experiences in dealing with different hotels like describing their opinions about different sides like cleanliness, food, employees, and other services. However, after COVID-19, new concerns have emerged linked to the health precautionary and preventive processes. At the same time, the amount of generated data (Textual Data) has become very huge (Big Data), and it needs to auto classifier for processing it where it is impossible to review manually. So, this research proposed a smart method to detect useful information from text by extracting the interesting services from comments automatically. The method depended on machine learning (ML) and compared between five different classification models (Spacy, Na&#x00EF;ve Bayes (NB), Stochastic Gradient Descent (SGD), Passive Aggressive, and AdaBoost) to determine which one provides the best results and accuracy. According to the experiment on a real dataset containing more than 1000 records, the NB was the best accurate with 98&#x0025;, while the Spacy was less and that related to the small size of training data.
Addressing Big Data Analytics Issues and Challenges Using Cloud Infrastructure

Data related terms are the buzzwords in research and academia in recent studies. Big Data, cloud computing, data analytics and artificial intelligence are some of the terms commonly seen in the studies. Big data and cloud computing integration opens many possibilities and opportunities to variety of organisations and businesses throughout the world. There are many cloud computing service providers offering different service and deployment model portfolios to choose from. Powerful analytical tools are being integrated with cloud computing such as Hadoop, Spark, and Storm to speed up the analytical process and maximise the benefit of cloud computing. This research reviews on the use of cloud computing in big data analytics and the challenges that comes with it. In this paper, different cloud computing models, available analytical tools and real-time data processing in cloud computing has been discussed with comparative analysis of the services providers. Finally, the challenges of the existing cloud computing solutions and future research scope is discussed.
Recognizing birds by image recognition

In this work, three algorithms (HOG with SVM, VGG, ResNet) are chosen to perform better image recognition on different categories of birds. HOG with SVM performs worse than ResNetwith SVM because HOG is better at recognizing objects than identifying categories of objects. Hyper-parameters in HOG and SVM will affect the accuracy. (Bochen). To improve the accuracy of the image recognition, the VGG algorithm is adopted with different hyper-parameters. Yujin Wang employs “Deep residual learning for image recognition” from He K, Zhang X, Ren S, et al to show his understanding of the ResNet algorithm. He will test different hyperparameters in ResNet and show readers how they will affect the results.
A Hybrid SVC-CNN based Classification Model for Handwritten Mathematical Expressions(Numbers and Operators)

Machine learning and Computer Vision are computer science domains that have been working closely for a long time. Given the ubiquity of handwritten text in human transactions, we are endeavoring to acquire the answer to the quest "Can Computer Vision and Machine learning together be deployed effectively for decisive classification of handwritten mathematical numbers and operators?". The easier it is to communicate via handwritten texts and documents, the more challenging the task of digitizing and prediction, especially for the two-dimensional complex math statements and operators. This paper presents a hybrid model that involves machine learning and deep learning-based decision algorithms for classifying and predicting mathematical numbers and operators. The dataset considered for the experimentation has been downloaded from the Kaggle dataset store consisting of more than 12K images. The primary tasks involved include data collection, data preprocessing, and building and deploying the model. Mainly our model focuses on the extraction of contour features and performing classification using the LinearSVC model, and the prediction of numbers has been accomplished using CNN. The proposed classification and prediction model achieves an accuracy of 89.76% for predicting the math operators and 91.48% for predicting the numbers.
Gradient-based Adversarial Attack Detection via Deep Feature Extraction

With the evolving adversarial attack landscape, deep neural networks (DNNs) remain susceptible to perturbed input that renders erroneous classification. Methods based on gradient obfuscation and adversarial training showed early success in increasing DNN robustness however, new attacks based on differential approximation show that hidden gradients can be recovered. In this paper, we propose gradient-based adversarial attack detection (GAAD) a robust adversarial attack detector that unmasks out-of-distribution inputs in image data to prevent mis-classification. Existing one-shot detectors are proven ineffective as they rely on a baseline model accuracy. Thus we present a two-shot framework where the detector is an independent network trained on salient meta-features that influences classification towards a specific class. The extracted meta-features are projected onto a stochastic distribution in the training submanifold to determine whether an input is "real" or "fake". We empirically show that GAAD exceeds state-of-the-art detectors across a wide range of adversarial attacks by achieving a high detection rate on standard benchmarks. Furthermore, we evaluate our method on the MNIST, Fashion-MNIST and CIFAR-10 datasets to show its robustness against new attacks.
Recognition of Kannada characters using deep learning approach

Recognition of Kannada Characters is a complex task as the number of classes in Kannada language by considering all combinations of vowels and consonants is 623,893. In this paper, the complexity is reduced from 623,893 to just having 313 classes as Main aksharas (Vowel, Consonants,Vowel modifiers and Consonant modifiers) and 30 classes as vattu aksharas(conjuncts) by using two line segmentation. A novel CNN model for recognition of printed and handwritten Kannada characters is proposed. CNN model with two, three and four layers are designed for Main akshara and Vattu aksharas with different filter size. The database consists of total of 31,300 samples and 3000 samples of printed and handwritten characters of Main akshara and Vattu aksharas respectively. Simulation result revealed that CNN model with four layer architecture is the best model for recognition of Kannada characters. This model achieved a recognition accuracy of 98.83% and 99.29% for printed Main akshara and Vattu aksharas and 82.50% and 80.92% for handwritten main and vattu akshara respectively.
Impaired behavior recognition based on multi-head-Siamese neural network

Sensor-based human behavior recognition is a classification recognition task and is widely used in medical care, environmentally assisted living, and other fields. But multiple sensors sense the impaired behavior without considering the correlation between sensors. In this paper, a multi-head-siamese neural network, combined with weight sharing is proposed based on deep learning theory. The network hyperparameters are adjusted by Bayesian optimization. Due to the problem of over-fitting during impaired behavior recognition introduced by Adam optimizer, L2 regularization is improved by using AdamW optimizer. Processing results of raw data show that the network achieves a classification accuracy of 96.0%. Compared with the baseline network and single input network, its accuracy has increased by 6.1% and 8.8% respectively. Compared with multiple input network, its accuracy has increased by 2.4%, and reduced the number of training parameters by 92%. Verified the effectiveness of the proposed network for impaired behavior recognition.
Prime Knowledge with Local Pattern Consistency for Knowledge Distillation

Intermediate feature maps of teacher model can produce enriched knowledge to improve the performance of student model. Existing works mainly focus on formulating beneficial knowledge for transferring, but ignore the contribution discrepancy of the knowledge to promote performance. To tackle this issue, we propose a simple Importance-based Knowledge Reweighting mechanism, which dynamically measure the importance of knowledge spatially and channel-wisely for teacher-student pairs. This reweighting scheme enables the student model to focus more on the prime knowledge. Furthermore, a local pattern consistency loss based on Structural Similarity Index Measure (SSIM) is presented to narrow the local pattern discrepancy between teacher and student features. Extensive experiments on CIFAR-100 with various combinations of network architectures for teacher and student well demonstrate the effectiveness and superiority of our proposed approach.
Enhancement of Face Detection with Email Facility

Present research work is focusing on multiple faces detection with an email notification facility. There have been many types of research that did work on face detection. But those researchers were unable to detect multiple faces efficiently. Thus face detection from the crowd is considered a great issue. In the present research, a real-time face recognition system is making use of an eigenvector mechanism to identify multiple faces from the crowd. A mail is transferred over mail when face detection is performed successfully. The proposed research is supporting three cases where the first case allows capturing of the single face from an image while the second case allows capturing of multiple face images from the crowd. Finally, the third case is allowing live image capturing with an eigenvector mechanism to detect multiple faces. Then the system is sending notifications by associated email id immediately after face detection. In this way, the proposed approach is providing an efficient solution for multiple face detection along with an email notification facility.
Effective management of class imbalance problem in climate data analysis using a hybrid of deep learning and data level sampling

Climate change and its consequences for human life have emerged as the world’s most pressing challenge. Due to the complexity, veracity, and velocity of climate data, a traditional, simple, and single machine learning model will not be sufficient to perform effective and timely analysis. The climate data can be effectively analyzed, and climate models can be developed with the proposed hybrid model. The deep learning AutoEncoder (AE) is used for feature extraction, removal of redundant and noisy data. The Synthetic Minority class Oversampling (SMOTE) technique to generate samples in minority class to mitigate the imbalance in the sample distribution. Extreme Learning Machine (ELM) is used for further feature classification. The proposed method exploits big data strategies and the results interpretation process to extract accurate insight from climate data. ELM handles the class imbalance problem to improve the performance of the Early Warning System (EWS) model and fine-tune it. The hybrid method drastically reduces the computation cost and improves the accuracy to 93%, 86%, 95%, and 98% of four different datasets against other machine learning models. The experimental results of the AE_SMOTE_ELM model, compared with other state-of-the-art deep learning methods, shows accuracy and an efficiency of 90.4% and 91.76%, respectively, for two climate datasets.
Dehazing algorithm based on multi-scale feature extraction

Fog seriously affects the visual perception of human eyes and reduces the quality of captured images. This paper proposes a dehazing Generative Adversarial Network based on multi-scale feature extraction. The method is an end-toend dehazing network that avoids the dependence on physical models. By adding the edge feature extraction module to the generator network to obtain the high-frequency information of the foggy image, the attention to the edge information of the image is effectively improved. In addition, the multi-scale features of the image are extracted, and then the foggy image is enhanced by a unique feature fusion mechanism. The discriminator network uses the global discriminator and the local discriminator to make a joint judgement, which further improves the dehazing performance. Compared with state-of-the-art approaches available in the literature, the algorithm proposed in this paper obtains better subjective and objective image quality evaluation on the cityscape foggy image synthesis dataset.
A Knowledge/Data Enhanced Method for Joint Event and Temporal Relation Extraction

Understanding temporal relations (TempRels) between events is an important task that could benefit many downstream NLP applications. This task inevitably faces the challenges of both a limited amount of high-quality training data and a very biased distribution of TempRels. These problems will substantially hurt the performance of extraction systems because they are inclined to predict dominant TempRels when training with a limited amount of data. To alleviate those issues, we propose a Knowledge/Data Enhanced method for Event and TempRel Extraction, which integrates the temporal commonsense knowledge, data augmentation and Focal Loss function into one single extraction system. Altogether, these components improve the performance of the system on two public benchmark datasets TB-Dense and MATRES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Removing rain from images via combining detection and removal

The performance of rain removal methods which are based on deep learning is largely affected by the designed models and training datasets for the image rain removal tasks. Most of current state-of-the-art focus on how to construct powerful deep models. But in this paper, we start from two perspectives of training dataset and model. We propose a novel rain model that includes a rain layer, a background layer and and a way how rainy image is generated. Based on this model, we develop a multi-task deep learning architecture that learns features of both the rain layer and the clean background layer. The additional information of rain layer is important because its loss function can provide additional powerful information to the network. Then we collected a large number of images of real rain streaks and outdoor scenes, and produced datasets for training. The effectiveness of our model and architecture was shown in tests on synthetic datasets.
A Feature Sparse Co-Attention Network for Visual internet of things (VIoT) sensing

Visual internet of things (VIoT) is a cross-modal learning task that requires a simultaneous understanding of image and question text information. The attention mechanism simulates human vision to retain the important part of the information and discard the unimportant part. However, since both important and unimportant features participate in the weighted summation, there is no true retention and discarding of information features. Furthermore, in many VIoT solutions, the utilization of the attention mechanism does not achieve the purpose of improving VIoT performance by aligning the key regions of image and question. To solve the problems mentioned above, this paper proposes an attention mechanism that sparse features to achieve attention with true retention and discard functions. At the same time, a feature sparse co-attention network is constructed to align the key regions of vision and text. The network is composed of image self-attention unit, question self-attention unit, and guiding attention unit. Each self-attention unit has a feature sparse function. These units are cascaded in depth to form a hierarchical structure that, as a whole, realizes co-attention. Several experiments conducted on the VQA-v2 dataset show that the proposed method outperforms the latest methods.
Downstream Augmentation Generation For Contrastive Learning

Contrastive learning has become one of the most promising approaches for learning image representations. However, it heavily relies on heuristic data augmentation techniques, such as Gaussian blurring and color jittering, for making image pairs to be contrastively compared. These augmentations are not always appropriate for downstream tasks that each have their own camera and illumination settings. In this paper, we aim at improving the augmentation process and propose an augmentation generator, a network that learns to augment images for contrastive learning. Under the assumption that each downstream task has an optimal implicit augmentation function, the augmentation generator enhances the contrastive learning by estimating it. We demonstrate the effectiveness of our learning framework on two combined datasets, EMNIST-Omniglot and ImageNet-DAISO.
Simulation research on anti-chopping and interleaving jamming based on PRF agility

Chopping and interleaving(C&I) jamming is a kind of very effective jamming, which has the effect of deception jamming and suppression jamming, and brings a great challenge to radar signal processing. In this paper, a new method based on PRF agility to anti C&I jamming is presented, which is analyzed theoretically and simulated numerically. The simulation results show that the C&I jamming can not disable the target tracking, and the range tracking error is reduced from 350m to nearly 20m.
Novel Deep Learning Approach for Scientific Literature Classification

Millions of research articles published each year require enhanced methods for both access and management. Text classification methods based on classical machine learning algorithms perform poorly for tasks involving hundreds of possible categories of research articles. Text classification has been revolutionized by the use of deep learning models such as convolution neural networks and recurrent neural networks. This paper demonstrates the applicability of hybrid approaches to the task of classifying scientific literature. A new hybrid approach that uses multiple word embeddings and a hybrid architecture is proposed. The proposed approach is evaluated on seven datasets. The proposed approach outperforms other methods on the task.
Implementation of digital marketing strategy in the Gaming industry

Digital Marketing is a power tool that can either break or make anything it touches. Due to the current pandemic scenario, we all have learned that Digitalisation is the new normal and adopting this new normal, digital marketing is "The Only Tool" for any marketer. Digital Marketing has not only changed the way how people viewed the gaming industry but has also opened new horizons for various kinds of career opportunities.Gaming industry is one of the most misunderstood and ignored industry of our modern world. Many people believe that gaming is just for lazy people who don&#x2019;t have anything better to do. But what most of them don&#x2019;t see is that Gaming industry is the biggest entertainment industry and even all other entertainment industry combined, can&#x2019;t match the annual revenue of gaming industry. I grew up playing video games and has seen this industry grow and change with time. My interest in gaming and newly developed passion for marketing, urged me to dive deeper into the world of digital marketing and how with evolution in technology and digital marketing strategies has impacted the gaming industry and brought it into the eyes of billions of people all around the globe.
Impact Analysis of Stacked Machine Learning Algorithms Based Feature Selections for Deep Learning Algorithm Applied to Regression Analysis

Ensemble learning algorithms have proved to be one of the best machine learning algorithms towards optimal performances in terms of regression and classification tasks for a variety of applications. When applied for small or medium structured datasets, eXtreme Gradient Boosting (XGBoost) has emerged as a popular ensemble learning technique based on its performance merits. In recent years Light Gradient Boosting Machine (LightGBM) has emerged as a promising ensemble strategy that is competing with XGBoost in terms of performance. Also, Lasso Regression has proven capabilities in terms of feature selections and applications for small datasets. This paper illustrates experimentation performed on a diabetes dataset where the authors tested the hypothesis that feature selection has relatively no impact on the performances of Deep Learning Algorithms as they have built-in capabilities in terms of layers to perform feature selection on their own. Therefore, the hypothesis tested stacking using Deep Learning &#x2013; Multi-Layer Perceptron (DMLP) with optimal algorithms like XGBoost, LightGBM, and Lasso Regression. In the present work, DMLP with all feature variables (DMLP-ALL) outscored DMLP with stacked selected features (DMLP-MS) by 8.78 % in terms of R<sup>2</sup>. Also, DMLP-ALL outperformed the benchmarked algorithm Automated Machine Learning (AML) by 10.25% in terms of R<sup>2</sup>. The validation of the proposed stacking models by applying a moderate-sized dataset provides promising results for deep learning models stacked with a powerful Level-0 learner.