Reviewerly/exHarmony huggingface dataset:
Time Complexity in Deep Learning Models

The convolution neural network is gaining a lot of popularity in image classification problems nowadays. It has been used in many different classification problems, like medical imaging, handwritten digits, image classification, etc. It is very critical to estimate the time required by the model to achieve the desired task. Earlier studies have used CNN for different perspectives and given the depth of each layer of CNN, but estimating the time and space taken by these layers is missing. This paper's prime idea is to find a CNN model's time complexity. The present work involves computational studies to find the factors that affect the model's performance, the time each layer takes to run, and how it affects the model's overall performance. Time complexity has been discovered on eight different models, varying by the size of filters, number of convolutional layers, number of filters, number of fully connected layers, and kernel size. The result shows that factors like an optimizer, batch size, filter, and neurons greatly impact the time taken by the model. The convolutional layers, max pool, and fully connected layers directly affect the performance of the model.
RANSAC for Robotic Applications: A Survey

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.
Governance and monetary policy impacts on public acceptance of CBDC adoption

Launching the central bank digital currency (CBDC) is increasingly recognized as a key priority by several governments. Unknown are, however, the primary factors of public support for the initiative and CBDC acceptance choices. Moreover, despite the fact that CBDC's success requires substantial public support, there is less empirical data of how the public perceives and speaks about it. This research seeks to fill in these gaps using Facebook data from May 2012 to April 2022 using deep learning algorithms for text mining. This research demonstrates that government performance, inflation rate, economic inequality, and technological literacy have a significant influence on the public's perception of CBDC. The government's support of CBDC is determined by public sentiment, the degree of adoption of decentralized finance (Defi), and monetary policy settings. The degree of wealth inequality and technological literacy are two other demographic elements that influence the government's adoption of CBDC.
Histogram of the orientation of the weighted phase descriptor for multi-modal remote sensing image matching

Multi-modal remote sensing image (MRSI) has nonlinear radiation distortion (NRD) and significant contrast differences to which image gradient features are usually sensitive. Although image phase features are more robust against NRD, they might not be much helpful in resolving the problems of directional inversion or phase extreme value mutations that are common in the phase feature calculation. To address these issues, a new MRSI matching method—“histogram of the orientation of weighted phase” (HOWP)—is proposed in this paper. This method distinguishes itself from other methods in three aspects: (1) a feature aggregation strategy is used to optimize feature points by extracting the corner and blob features separately; (2) a novel weighted phase orientation model is established to replace the traditional image gradient orientation features; and (3) a regularization-based log-polar descriptor is constructed to generate robust feature description vectors. To evaluate the performance of the proposed method, we selected 50 sets of typical MRSIs with translation, scale, and rotation differences for comparison with the other four state-of-the-art methods. The results show that our method is more resistant to radiometric distortion and the contrasting differences in MRSIs. It also performs better in tackling the problems of direction reversal and phase extreme value mutation, as evidenced by more, the number of correct matches (NCM). Since the method has improved the average NCM by 1.6–4.5 times, the average success rate by 35.5%, and the average rate of correct matches by 11.1% with an average root of mean-squared error of 1.93 pixels. Moreover, we have put forward an extended version of the HOWP method (Simplified-HOWP) when there is no image rotation, which manifests in an average 0.75 times improvement in NCM of Simplified-HOWP performance over that of the HOWP method. The executable code and test data are linked in https://skyearth.org/publication/project/HOWP/.
Generating Image Captions Using Bahdanau Attention Mechanism and Transfer Learning

Automatic image caption prediction is a challenging task in natural language processing. Most of the researchers have used the convolutional neural network as an encoder and decoder. However, an accurate image caption prediction requires a model to understand the semantic relationship that exists between the various objects present in an image. The attention mechanism performs a linear combination of encoder and decoder states. It emphasizes the semantic information present in the caption with the visual information present in an image. In this paper, we incorporated the Bahdanau attention mechanism with two pre-trained convolutional neural networks—Vector Geometry Group and InceptionV3—to predict the captions of a given image. The two pre-trained models are used as encoders and the Recurrent neural network is used as a decoder. With the help of the attention mechanism, the two encoders are able to provide semantic context information to the decoder and achieve a bilingual evaluation understudy score of 62.5. Our main goal is to compare the performance of the two pre-trained models incorporated with the Bahdanau attention mechanism on the same dataset.
Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture

The speech and hearing-impaired community use sign language as the primary means of communication. It is quite challenging for the general population to interpret or learn sign language completely. A sign language recognition system must be designed and developed to address this communication barrier. Most current sign language recognition systems rely on wearable sensors, keeping the recognition system unaffordable for most individuals. Moreover, the existing vision-based sign recognition frameworks do not consider all of the spatial and temporal information required for accurate recognition. A novel vison-based hybrid deep neural net methodology is proposed in this study for recognizing Indian and Russian sign gestures. The proposed framework is aimed to establish a single framework for tracking and extracting multi-semantic properties, such as non-manual components and manual co- articulations. Furthermore, spatial feature extraction from the sign gestures is deployed using a 3D deep neural net with atrous convolutions. The temporal and sequential feature extraction is carried out by employing attention-based Bi-LSTM. In addition, the distinguished abstract feature extraction is done using the modified autoencoders. The discriminative feature extraction for differentiating the sign gestures from unwanted transition gestures is done by leveraging the hybrid attention module. The experimentation of the proposed model has been carried out on the novel multi-signer Indo-Russian sign language dataset. The proposed sign language recognition framework with hybrid neural net yields better results than other state-of-the-art frameworks.
The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images

Supplemental material is available for this article.Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.
Improving the Imperceptibility of Adversarial Examples Based on Weakly Perceptual Perturbation in Key Regions

Deep neural networks have been proved vulnerable to being attacked by adversarial examples, which have attracted extensive attention from researchers. Existing GAN-based object detection adversarial example generation methods are efficient in generating speed but ignore the visual imperceptibility of adversarial examples. In this paper, to improve the visual imperceptibility of adversarial examples, we propose an object detection adversarial example generation method based on weakly perceptual perturbations in key regions. First, a positioning module based on the gradient-weighted activation mapping method is designed to analyze the key region of the object from the perspective of gradient propagation and use the key region in order to limit the range and amplitude of the perturbation. Second, the deep feature of the convolutional network is introduced to constrain the content of adversarial perturbation and improve the similarity between adversarial examples and original images. Finally, a postprocessing method based on median filtering is introduced to further correct the color deviation of adversarial examples and improve imperceptibility. The experimental results for VOC datasets show that the attack success rate increased by 4%, the PSNR increased by 8.6%, and the MSE and LPIPS decreased by 92.3% and 59.5%, respectively. It demonstrates that the proposed method can significantly improve the imperceptibility of the adversarial example with a high attack success rate.
SurvSHAP(t): Time-dependent explanations of machine learning survival models

Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanations are available to overcome this issue; however, none directly explain the survival function prediction. In this paper, we introduce SurvSHAP(t), the first time-dependent explanation that allows for interpreting survival black-box models. It is based on SHapley Additive exPlanations with solid theoretical foundations and a broad adoption among machine learning practitioners. The proposed methods aim to enhance precision diagnostics and support domain experts in making decisions. Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME. SurvSHAP(t) is model-agnostic and can be applied to all models with functional output. We provide an accessible implementation of time-dependent explanations in Python at http://github.com/MI2DataLab/survshap.
Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer

The most widespread and intrusive cancer type among women is breast cancer. Globally, this type of cancer causes more mortality among women, next to lung cancer. This made the researchers to focus more on developing effective Computer-Aided Detection (CAD) methodologies for the classification of such deadly cancer types. In order to improve the rate of survival and earlier diagnosis, an optimistic research methodology is required in the classification of breast cancer. Consequently, an improved methodology that integrates the principle of deep learning with metaheuristic and classification algorithms is proposed for the severity classification of breast cancer. Hence to enhance the recent findings, an improved CAD methodology is proposed for redressing the healthcare problem. The work intends to cast a light-of-research towards classifying the severities present in digital mammogram images. For evaluating the work, the publicly available MIAS, INbreast, and WDBC databases are utilized. The proposed work employs transfer learning for extricating the features. The novelty of the work lies in improving the classification performance of the weighted k-nearest neighbor (wKNN) algorithm using particle swarm optimization (PSO), dragon-fly optimization algorithm (DFOA), and crow-search optimization algorithm (CSOA) as a transformation technique i.e., transforming non-linear input features into minimal linear separable feature vectors. The results obtained for the proposed work are compared then with the Gaussian Naïve Bayes and linear Support Vector Machine algorithms, where the highest accuracy for classification is attained for the proposed work (CSOA-wKNN) with 84.35% for MIAS, 83.19% for INbreast, and 97.36% for WDBC datasets respectively. The obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the severity classification of breast cancer.
GRU-Based Interpretable Multivariate Time Series Anomaly Detection in Industrial Control System

Interpretable multivariate time series anomaly detection is an important technology to prevent accidents and ensure the reliable operation of Industrial Control Systems. A key limitation lies in the lack of a model to achieve better detection performance and more reliable interpretability, and keep a balance between performance efficiency and training optimization. In this paper, we propose GRN, an Interpretable Multivariate Time Series Anomaly Detection method based on neural graph networks and gated recurrent units (GRU). GRN can automatically learn potential correlations between sensors from multidimensional industrial control time series data, quickly mine long-term and short-term dependencies, to improve detection performance and help users to infer the root cause of detected anomalies. Based on GRU, GRN preserves the original advantages of processing the sequences and capturing the time series dependencies, moreover solves the problem of gradient disappearance and gradient explosion. We compare the performance of nine state-of-the-art algorithms on two real water treatment datasets (SWaT, WADI). GRN achieves better detection precision and recall. Meanwhile, the comparison of Area Under the Curve (AUC) demonstrates that GRN has the effect of maintaining balance between detection performance and training optimization. Compared with a Graph Deviation Network(GDN), GRN has achieved greater interpretability.
Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology

In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.
Decomposition integration and error correction method for photovoltaic power forecasting

Photovoltaic power generation has remarkable environmental benefit, and it is one of the effective means to fundamentally solve environmental problem. An accurate photovoltaic power forecasting system is critical for short-term photovoltaic power station scheduling and power generation plan operation. Aiming at the problems of high randomness and low prediction accuracy of photovoltaic power, a multi-factor forecasting model for photovoltaic power based on fuzzy C-means (FCM), improved variational mode decomposition (VMD) by white shark optimizer (WSO), long short-term memory (LSTM) optimized by artificial gorilla troops optimizer (GTO), error correction (EC), named WVMD-GTO-LSTM-EC, is proposed. First, screen out the main meteorological factors affecting photovoltaic power by Spearman correlation coefficient, and use these factors as clustering feature vector of FCM, divide photovoltaic power by FCM, and obtain similar daily samples. Second, improved VMD by WSO, named WVMD, is proposed to decompose similar daily samples of different meteorological models to obtain intrinsic mode functions (IMFs). Third, LSTM optimized by GTO, named GTO-LSTM, is proposed, and the IMFs combined with meteorological factors data are input into GTO-LSTM for training and testing. Final, EC is established to further increase the forecasting precision and the evaluation index, correlation coefficient R and DM test are introduced to comprehensively evaluate the model. In this paper, to demonstrate the superiority of proposed model, two groups of photovoltaic power data gathered by an Australian solar power generation system are used as experimental data, and the prediction performance is compared with that of eight comparison models. The experimental results show that forecasting value of this model accords with true value to the greatest extent, R = 0.99, and MAPE, MAE, RMSE and R2 can reach 0.02, 0.09, 0.12 and 0.99, respectively, which indicates that its forecasting ability is superior to all comparison models.
A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine learning models: logistic regression, random forest, and decision trees to classify, predict, and detect fraudulent credit card transactions. We compare these models’ performance and show that random forest produces a maximum accuracy of 96% (with an area under the curve value of 98.9%) in predicting and detecting fraudulent credit card transactions. Thus, we recommend random forest as the most appropriate machine learning algorithm for predicting and detecting fraud in credit card transactions. Credit Card holders above 60 years were found to be mostly victims of these fraudulent transactions, with a greater proportion of fraudulent transactions occurring between the hours of 22:00GMT and 4:00GMT.
Applications of Internet of Things in Smart Grid Intelligent Systems

Smart meters, smart appliances, renewable energy sources, and energy-efficient resources are only some of the operational and energy-efficiency elements of a “smart grid.” Smart grids rely heavily on electronic power conditioning and control of energy production and delivery. An intelligent generation, transmission, distribution, and consumption are the main components. Human-to-human contact is now the major way of communication on the Internet. The Internet of Things will replace the Internet. This technology has the potential to be used to the construction of smart grids. Smart grid research and development result in new technologies that makes life simpler for humans. This article provides an in-depth examination of the different technologies and standards for smart grids. This article explains the smart grid and innovative concepts such as electric vehicles and automated electric car charging on public roadways
Federated Learning and Blockchain-Enabled Fog-IoT Platform for Wearables in Predictive Healthcare

Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network’s distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform’s ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient’s privacy and a predictive service’s integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.
CenterNet-Auto: A Multi-object Visual Detection Algorithm for Autonomous Driving Scenes Based on Improved CenterNet

With the rise in popularity of autonomous driving, the speed and accuracy of surrounding objects' detection by in-vehicle sensing technology is becoming increasingly important for autonomous vehicles. Building on CenterNet, this paper proposes CenterNet-Auto, a new anchor-free detection network for driving scenes that can satisfy the detection speed requirements while ensuring detection accuracy. The network's backbone uses the RepVGG model transformed through structural re-parameterization technology. Features of different scales are fused, and feature pyramids and deformable convolution are added after the backbone to accurately detect objects of different sizes. To solve the occlusion problem in the driving scene, this paper proposes the Average Border Model, which supports locating the object using the boundary feature information. The test results demonstrate that the proposed algorithm outperforms CenterNet regarding speed and accuracy on the BDD dataset. The accuracy reaches 55.6%, and the speed reaches 30 FPS, meeting the speed and accuracy requirements in a driving scene.