Reviewerly/exHarmony huggingface dataset:
Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches

In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. A novel application of Variational Bayesian inference approach has been presented to estimate the heart sources. The reconstruction results of Variational Bayesian model with non-stationary priors are compared with solutions of simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The Bayesian solutions in the study are also extended to localize the MCG sources for two types of Myocardial infarction cases.
Two‐stage learning framework for single image deraining

Abstract Single image deraining methods have been extensively studied for its ability to remarkably improve the performance of computer vision tasks in rainy environments. However, most existing rain removal methods still have two major drawbacks which are hindering the technology development. First, the rain streaks are seriously coupled with the background information in a single rainy image, which leads to incorrect identification of rain streaks by many methods and further makes the loss of texture details in the rain removal results. Second, they spend excessive computational cost, which is not conducive to practical applications. To address these issues, a progressive separation network (PSN) is proposed by decomposing the rain removal task into two stages, the bilateral grid learning stage and the joint feature refinement stage, from a novel perspective. The bilateral grid learning stage is designed to expand the distance between the rain streaks and the background information while preserving the image edge details to guide the subsequent refinement. For the joint feature refinement stage, a dual‐path interaction module is constructed to dynamically and gradually decouple the rain streak content and the intermediate features of the clear image details. In addition, an activation‐free feature refinement block is designed to further improve the computational efficiency by removing or replacing the activation function without loss of accuracy. Extensive experiments on synthetic and real datasets show that PSN outperforms state‐of‐the‐art rain removal methods in terms of quantitative accuracy and subjective visual quality. Furthermore, competitive results are derived by extending PSN to the defogging task.
Independency‐enhancing adversarial active learning

Abstract The core idea of active learning is to obtain higher model performance with less annotation cost. This paper proposes an independency‐enhancing adversarial active learning method. Independency‐enhancing adversarial active learning is different from the previous methods and pays more attention to sample independence. Specifically, it is believed that the informativeness of a group of samples is related to sample independence rather than the simple sum of the informativeness of each sample in the group. Therefore, an independent sample selection module based on hierarchical clustering is designed to ensure sample independence. An adversarial approach is used to learn the feature representation of a sample and use the predicted loss value to label the state of the sample. Finally, samples are selected according to the uncertainty of the samples, the diversity of the samples and the independence of the samples. The experimental results on four datasets (CIFAR‐100, Caltech‐101, Cityscapes and BDD100K) demonstrate the effectiveness and superiority of independency‐enhancing adversarial active learning.
Enhancing female status by improving nutrition: the role of corporate social responsibility in Nigeria’s oil region

Purpose The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on improving female status by improving nutrition in the Niger Delta region of Nigeria. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 768 women respondents were sampled across the rural areas of the Niger Delta region. Findings The results from the use of a combined propensity score matching and logit model indicate that GMoU model has made significant impact in the key areas of assessment – gender-sensitive nutrition education, food security at household level, reduction on food taboos and female access to education. Practical implications This suggests that CSR interventions targeting to improve the nutrition status of girls and adolescents will help to ensure that female’s status improves throughout the life circle in the region. Social implications This implies that MOCs’ investment in the nutrition of female is an important short-term barometer in assessing expected returns to improving household nutrition and overall human development capacity for sub-Saharan Africa. Originality/value This research contributes to the inequality debate in the women’s nutrition and inclusive growth literature from the CSR perspective. It concludes that business has an obligation to help in solving problems of public concern.
Adaptive fuzzy‐region growing fusion and improved <scp>CNN‐ANFIS</scp>‐based automated segmentation and classification of cervical cancer

Summary The cervical cancer screening mechanisms help to classify cervical lesions into cancerous, precancerous, and normal. But, some of the classification approaches provide optimal outcomes based on the precise spotting and segmenting of cervical cancer. Moreover, still, challenges persist in the precise spotting of cervical cancer. The conventional screening methods for cervical cancer require more experienced pathologists, but it may also lead to poor efficiency and low accuracy. To overcome these issues, medical image processing introduces both machine learning and deep learning methods into cervical cell classification. The major scope of this task is to plan a new cervical cancer diagnosis method using intelligent approaches. Here, the segmentation of abnormality is done by the novel adaptive fuzzy‐region growing fusion model, which is improved by the hybridized optimization algorithm named gray wolf‐tunicate swarm algorithm (GW‐TSA). Then, the new ensemble classification termed Improved convolutional neural network with artificial neural network fuzzy inference system, which depends on the proposed GW‐TSA for performance enhancement through optimizing the hyperparameters. Finally, the simulation findings show that the designed approach is achievable and provides the highest accuracy rate in diagnosing cervical cancer.
SMSG: Profiling-Free Parallelism Modeling for Distributed Training of DNN

Abstract The increasing size of deep neural networks (DNNs) raises a high demand for distributed training. An expert could find good hybrid parallelism strategies, but designing suitable strategies is time and labor-consuming. Therefore, automating parallelism strategy generation is crucial and desirable for DNN designers. Some automatic searching approaches have recently been studied to free the experts from the heavy parallel strategy conception. However, these approaches all rely on a numerical cost model, which requires heavy profiling results that lack portability. These profiling-based approaches cannot lighten the strategy generation work due to the non-reusable profiling value. Our intuition is that there is no need to estimate the actual execution time of the distributed training but to compare the relative cost of different strategies. We propose SMSG (Symbolic Modeling for Strategy Generation), which analyses the cost based on the communication and computation semantics. With SMSG, the parallel cost analyses are decoupled from hardware characteristics. SMSG defines cost functions for each kind of operator to quantitatively evaluate the amount of data for computation and communication, which eliminates the heavy profiling tasks. Besides, SMSG introduces how to apply functional transformation by using the Third Homomorphism theorem to control the high searching complexity. Our experiments show that SMSG can find good hybrid parallelism strategies to generate an efficient training performance similar to the state of the art. Moreover, SMSG covers a wide variety of DNN models with good scalability. SMSG provides good portability when changing training configurations that a profiling-based approach cannot.
Hypergraph-based importance assessment for binary classification data

Abstract We present a novel hypergraph-based framework enabling an assessment of the importance of binary classification data elements. Specifically, we apply the hypergraph model to rate data samples’ and categorical feature values’ relevance to classification labels. The proposed Hypergraph-based Importance ratings are theoretically grounded on the hypergraph cut conductance minimization concept. As a result of using hypergraph representation, which is a lossless representation from the perspective of higher-order relationships in data, our approach allows for more precise exploitation of the information on feature and sample coincidences. The solution was tested using two scenarios: undersampling for imbalanced classification data and feature selection. The experimentation results have proven the good quality of the new approach when compared with other state-of-the-art and baseline methods for both scenarios measured using the average precision evaluation metric.
Research on Continued Intention to Adopt E-Learning in Beijing University During Covid-19 Epidemic in China

The study aims to apply the TAM model to investigate further about the opinions of students at Peking University on the e-learning platform provided by the university. The research validates the impact of the different variables on students’ intention to continue using e-learning platforms during the Covid-19 epidemic, including perceived usefulness, perceived ease of use, perceived enjoyment, and perceived convenience. The research adopts a quantitative research method and measures the variables using a 5-level Likert scale. The designed questionnaires are distributed online to the targeted respondents through multiple social applications. The research collects 383 valid data and conducts a comprehensive data analysis by using SPSS analysis software. The research indicates that perceived usefulness, perceived ease of use, and perceived convenience all have a significant and positive impact on student’s intention to continue using e-learning platforms. However, this research also confirms that perceived enjoyment has no significant effect on student’s intention to continue using e-learning platforms. Therefore, this research validates the hypotheses proposed and achieves the research goals. Furthermore, the research provides useful information and recommendations for the government, education field, IT field and future research.
The AgroCart Android Application to Manage Agriculture System

This system provides its users to get online information about the market requirements and guide farmers to grow the crops according to the market's need. This application has 4 modules, first is the Pre-Production module, where this application guides users to grow the crop according to market requirements. Second is the buying and selling module, where customers can register for the application by entering profile details. The customers can purchase products that are uploaded by the admin, and in the selling part, farmers are the sellers where they can sell their products online. The third is the blogs module, which helps farmers to use fertilizers to get a better yield of crops. And the fourth module is smart agriculture; it consists of a temperature sensor, moisture sensors, and pest detection.
Highly Classified with Two Factor Authentication Encrypted Secured Mail

Electronic mail which is used to send messages between Internet connected devices works following simple mail transfer protocol (SMTP). The massages can contain text, images, audio clip, video clip, etc., with limited size. Each user account will be having its own credentials to login, once user logged in various options are available like reading messages in inbox also message can be sent to other accounts. When mail has sent from one system to another system, message will divided into small packets. These independent packets reach destination following routing algorithm applies at that time. At destination, all packets belong to particular mail will gather together and rearrange as they left at source system. In this scenario, there are possibilities of copying the packets when they are traveling through routers. This paper provides second-level security using strong garage with different buffers for each transactions.
Optical Character Recognition System of Telugu Language Characters Using Convolutional Neural Networks

In pattern recognition, recognition of characters plays a crucial role. Optical Character Recognition (OCR) is a challenging problem for many decades. Identifying handwritten characters is an easy task for humans, but it is hard for a computer. Exploration in character recognition is very popular for many applications such as in banks, processing of languages, library automation, and designs of multi-media. An OCR system contains an optical scanner to scan the text and advanced software for interpreting images. Recognition of Handwritten Telugu Characters is strenuous as discrete handwritten scripts may have discrete styles of writings, slants, etc. The main purpose of optical character recognition is to make editable documents or images. In this paper, we have used deep learning techniques like Convolutional Neural Networks for recognizing Telugu characters.
AI-Based Live-Wire News Categorization

Nowadays globally, an immense amount of daily news is generated by a lot of different sources. The need and the demand for information by users have been increasing continuously, so it is important for the news to be classified in order to allow people to access the information of viewer interest and beneficially. News is generated from different sources across the globe. The sources may be the internet, news wires, or local news. The news channels, print media, news delivering channels will broadcast or deliver news on categories, say, Political, Sports, Entertainment, Science and Technology, or any other types. Thus, our goal is to develop models that take input as news headlines and short descriptions and output the news category. The live stream news will be divided into different categories of the configuration by news channels or other means of delivering the news to common public, using machine learning tools. To classify the live-wire stream news into different categories of interest of news channels, print media, internet news portals is by extracting desired categories for onward transmission in broadcast or print media using machine learning and deep learning tools.
Accelerating Real-Time Face Detection Using Cascade Classifier on Hybrid [CPU-GPU] HPC Infrastructure

Real-time face detection finds various applications in recent trends like robotics, surveillance, law enforcement, interactive game application, etc. To detect the face in real-time with CPU-based high-performance computing [HPC] infrastructures like cloud and clusters consumes a lot of time, and even despite numerous optimization methods, it achieves poorly in real-time. However, to process a huge amount of real-time video, data at tremendous speed requires the hybrid [CPU-GPU] HPC infrastructure. In this study, we have considered the power full hybrid [CPU-GPU] HPC infrastructure; it combines both multi-core CPU and many-core GPU with the advantage of parallel programming paradigm CUDA on GPU and OpenCV on CPU due to this the performance of real-time face detection is faster. For a face detection strategy in this work, we have proposed Viola and Jones algorithm with a cascade classifier. Proposed algorithms are tested on both the infrastructures, i.e., CPU-based HPC infrastructure and hybrid [CPU-GPU] HPC infrastructure. We have observed that real-time face detection processing time on hybrid [CPU-GPU] HPC infrastructure has achieved an average of 18.91 frames per second and average detection time up to 0.64 times computational speed up based on varying resolutions from 640 × 480 to 2048 × 1536 when compared against CPU-based HPC infrastructure.
A Comprehensive Review on the Issue of Class Imbalance in Predictive Modelling

One of the factors that affect the performance of the classification predictive modelling task is the skewed distribution of the classes in the training data, which is known as the "class imbalance problem". It hinders the performance of the predictive models. The contribution of this paper is threefold; primarily, this paper discusses the latest techniques used in different domains to handle class imbalance. Then, the taxonomy of data preprocessing, algorithm level and hybrid techniques have been presented. Finally, various evaluation strategies and application domains are presented. This paper covers all the latest and most effective techniques and elaborates on effective techniques such as cost-sensitive learning and ensemble learning, which collectively provide the reader with an extensive overview of the techniques used by researchers to alleviate skewed class distribution of the classes in the training data.
Structure and Base Analysis of Receptive Field Neural Networks in a Character Recognition Task

This paper explores extensions and restrictions of shallow convolutional neural networks with fixed kernels trained with a limited number of training samples. We extend the work recently done in research on Receptive Field Neural Networks (RFNN) and show their behaviour using different bases and step-by-step changes within the network architecture. To ensure the reproducibility of the results, we simplified the baseline RFNN architecture to a single-layer CNN network and introduced a deterministic methodology for RFNN training and evaluation. This methodology enabled us to evaluate the significance of changes using the (recently widely used in neural networks) Bayesian comparison. The results indicate that a change in the base may have less of an effect on the results than re-training using another seed. We show that the simplified network with tested bases has similar performance to the chosen baseline RFNN architecture. The data also show the positive impact of energy normalization of used filters, which improves the classification accuracy, even when using randomly initialized filters.
ECSS: High-Embedding-Capacity Audio Watermarking with Diversity Reception

Digital audio watermarking is a promising technology for copyright protection, yet its low embedding capacity remains a challenge for widespread applications. In this paper, the spread-spectrum watermarking algorithm is viewed as a communication channel, and the embedding capacity is analyzed and modeled with information theory. Following this embedding capacity model, we propose the extended-codebook spread-spectrum (ECSS) watermarking algorithm to heighten the embedding capacity. In addition, the diversity reception (DR) mechanism is adopted to optimize the proposed algorithm to obtain both high embedding capacity and strong robustness while the imperceptibility is guaranteed. We experimentally verify the effectiveness of the ECSS algorithm and the DR mechanism, evaluate the performance of the proposed algorithm against common signal processing attacks, and compare the performance with existing high-capacity algorithms. The experiments demonstrate that the proposed algorithm achieves a high embedding capacity with applicable imperceptibility and robustness.
Towards Building a Distributed Virtual Flow Meter via Compressed Continual Learning

A robust-accurate estimation of fluid flow is the main building block of a distributed virtual flow meter. Unfortunately, a big leap in algorithm development would be required for this objective to come to fruition, mainly due to the inability of current machine learning algorithms to make predictions outside the training data distribution. To improve predictions outside the training distribution, we explore the continual learning (CL) paradigm for accurately estimating the characteristics of fluid flow in pipelines. A significant challenge facing CL is the concept of catastrophic forgetting. In this paper, we provide a novel approach for how to address the forgetting problem via compressing the distributed sensor data to increase the capacity of the CL memory bank using a compressive learning algorithm. Through extensive experiments, we show that our approach provides around 8% accuracy improvement compared to other CL algorithms when applied to a real-world distributed sensor dataset collected from an oilfield. Noticeable accuracy improvement is also achieved when using our proposed approach with the CL benchmark datasets, achieving state-of-the-art accuracies for the CIFAR-10 dataset on blurry10 and blurry30 settings of 80.83% and 88.91%, respectively.
Toward deep neural networks robust to adversarial examples, using augmented data importance perception

Deep neural networks (DNNs) have been shown to be susceptible to slightly perturbed adversarial examples. Even adversarial examples that are imperceptible to the human eye can easily lead to misclassification of DNNs. The most effective defense against adversarial examples is adversarial training (AT). Through AT, the robustness of the model against adversarial examples can be greatly improved. However, AT causes a decrease in the accuracy of the model’s classification of natural examples and serious overfitting problems. To improve the generalization ability of the model, we propose a method for enhancing model robustness through augmented data importance perception (ADIP). By extracting offensive adversarial examples for data augmentation and considering the attack strength of new adversarial examples to design an importance measure term to improve the loss function, the results of experiments show that the model robustness enhancement method based on ADIP improves the robustness of the model and alleviates the overfitting problem of the model caused by AT. In addition, our algorithm trains a robust model with a small computational cost.