Reviewerly/exHarmony huggingface dataset:
Neutrosophic Based Image Relationship Analysis for Fingerprint Image Pattern Recogniton and Matching

Fingerprint matching is a severe pattern-recognition problem because of large variations in fingerprint images of the same finger and similarity between fingerprint images from different fingers, known as intraclass variations and interclass similarity. Fingerprint-matching algorithms embrace one of four approaches image correlation, phase matching, skeleton matching, and minutiae matching. The fuzzy concept was previously implemented successfully in the fingerprint matching concept. Neutrosophic is the advanced mathematical model that extends the fuzzy concept to solve real-world problems and applications. Through the imageâ€™s neutrosophic relationship, it is possible to find the correlation pattern of the image. Via neutrosophic-based pattern analysis, this article will apply the neutrosophic technique to find the correlation between the fingerprint images. This article will propose six methods for finding the relationship based on the neutrosophic technique in the image domain data. The article will approach three types of analysis methodologies; the first analysis methodology is
Automatic Forecasting of Volcanoes Eruption Time

Detecting volcanic eruptions before they occur is a significant issue that has traditionally proved to be challenging. However, it is an interesting concern because of its great potential in saving millions of lives by accurately forecasting the eruption time of volcanoes in the proximity of the inhabited areas, which would provide additional time and facilitate the evacuation processes of people early enough before the catastrophe. In order to forecast the eruption of volcanoes, 10 sensors were mounted around 4431 volcanoes to monitor the eruption time. The goal is to estimate the time of the eruption of another 4520 volcanoes in the coming years. Now that we have established the problem, our approach is formulated using various regression models of machine learning to see the best solution for prediction. These are: Random Forest (RF), Artificial Neural Network (ANN), Convolution Neural Network (CNN), and Long ShortTerm Memory (LSTM). We used the Mean Absolute Error (MAE) as our loss function as well as performance metric, since the time to erupt values are large, the loss will be as well. We have labeled data for training and validation, and unlabeled data for testing. The best unlabeled loss is 6,042,665 belongs to CNN model. Finally, we analyzed the results and compared between the four models and found that RF doesn&#x2019;t work well with noisy data. On the other hand LSTM, ANN, and CNN have nearly the same behaviour but the latter gave better results.
Research on underwater image registration method based on MSRCR and SIFT

Due to the complex and varying underwater environment, the optical images obtained are low resolution and full of noise, which ultimately make it difficult to register and stich the optical images. To solve the above problems, we propose an improved image registration method based on MSRCR and SIFT. First, multi-scale retinex with color restoration (MSRCR) is applied to improve the underwater low-quality image, and the image contrast is improved by contrast limited adaptive histogram equalization (CLAHE). After that, the scale-invariant feature transform (SIFT) algorithm is adopted to extract feature points of the reference image and sensed image. Then coarsely match the feature points based on the K nearest neighbor (KNN)-based Ratio Matching algorithm, and the random sample consensus (RANSAC) is used to eliminate mismatched feature points and improve matching accuracy. Finally, the mosaic image is output after the transformation matrix is calculated. The experiment results show a better effect of underwater image registration and stitching, demonstrating that the algorithm can improve the accuracy of underwater image registration through image enhancement.
The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices

Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors. One of the most important components of SI is feature extraction, which has a substantial impact on the SI process and performance. As a result, numerous feature extraction strategies are thoroughly investigated, contrasted, and analyzed. This article exploits five distinct feature extraction methods for speaker identification in disguised voices under emotional environments. To evaluate this work significantly, three effects are used: high-pitched, low-pitched, and Electronic Voice Conversion (EVC). Experimental results reported that the concatenated Mel-Frequency Cepstral Coefficients (MFCCs), MFCCs-delta, and MFCCs-delta-delta is the best feature extraction method.
Pre-Activation - Spatial Attention Module for Multiple Catheters and Tubes Classification

Catheters and tubes are used in emergency departments and intensive care units to keep severely ill patients alive. The proper catheter or tube placement is essential to avoid serious complications. Such issues can be solved if they are found early. The catheters are usually assessed by chest x-rays. Attention modules have recently demonstrated the benefit of improving spatial encoding in multi-label classification tasks on chest X-ray images. In this paper, we propose a Pre-Activation-Spatial Attention (PAc-SA) module. It improves the spatial attention module for multiple catheters and tubes image classification efficiently. In its computation, the proposed module used BN and ReLU as pre-activations. Experiments on the RANCZR-CLiP dataset show that the proposed PAc-SA module achieves better than others, with the best accuracy score of 94.65% and the highest average precision score of 65.18%, respectively, and reduces the network model parameters.
Information Theoretic Approach to L-Estimators

We propose a novel way of choosing the coefficients of a class of robust estimators, known as L-estimators. Towards this end, we leverage information theoretic measures, such as the entropy and mutual information, to rigorously characterize the amount of information contained in any subset of the complete collection of order statistics. As an application, we show how the developed framework can be used for image denoising. In particular, we demonstrate that the proposed method is competitive with off-the-shelf filters, as well as with wavelet-based denoising methods, for both discrete (e.g., salt and pepper) and continuous (e.g., mixed Gaussian) noise distributions.
A Hierarchical Motion Retrieval Algorithm for Complex Manipulation Tasks Planning with An Encoded Knowledge Base

In human-robot cooperation, it is a challenge thing that the robot should perform to convert humans' natural languages to continuous action sequences, which is necessary for completing complex collaborative tasks. In this paper, firstly, a new knowledge base is built for encoding different features of movements, objects and relations; then, a hierarchical motion sequences retrieval algorithm is presented by combining our knowledge base with Deep Q-learning. Finally, the experiments verify that the developed reasoning system is effective and accomplishes to manipulate the objects to reach target statuses.
A Color Vision Approach Considering Weather Conditions Based on Autoencoder Techniques Using Deep Neural Networks

Color machine vision is a riveting technology crucial in pioneering innovations like autonomous vehicles, autonomous drone deliveries, automated stores, robots, infrastructure and surveillance monitoring programs for security, manufacturing defect monitoring and more. When it comes to real life applications of automated machines, safety is a major concern and to ensure utmost safety the unpredictable has to be taken into consideration. We propose and demonstrate a color vision approach that allows image normalization hinged on autoencoder techniques employing deep neural networks. The model is composed of image preprocessing, encoding and decoding. The images are resized in preprocessing portion the images go through a cognitive operation where the input image becomes suitable to enter the autoencoding technique section. The autoencoder is comprised of two core components &#x2013; encoder and decoder. To employ this system deep neural network is applied which generates a code of an image in the encoding process. Sequentially, the code changes over to decoding. Decoder portion decodes it and regenerates the initial image extracting it from the code of the encoder portion. It allows normalizing color images under different weather conditions such as images captured during rainy or foggy weather conditions. We devise it such that rainy and foggy images are normalized concurrently and in real time. The autoencoder is trained with numerous rainy and foggy datasets utilizing CNN. In this research we investigate the model normalizing images in two different weather conditions &#x2013; rainy and foggy conditions in real time. We used SSIM and PSNR to verify the accuracy of the model and confirm its capability reconstructing images in real time for advanced real life color vision implementations.
Multi‐scale event causality extraction via simultaneous <scp>knowledge‐attention</scp> and convolutional neural network

Abstract Event causality extraction is a challenging task in natural language processing (NLP), which plays an important role in event prediction, scene generation, question answering and textual entailment. Most existing methods focus on extracting single‐scale (such as phrase) event causality, while fails to extract multi‐scale (such as word, phrase, sentence) event causality. To fill the gap, we propose multi‐scale event causality extraction via simultaneous knowledge‐attention and convolutional neural network (KA‐CNN). First, knowledge‐attention takes N‐gram embedding as input and takes semantic features, fused with prior knowledge through causal associative link network (CALN), as output. Second, multi‐scale CNN is designed with word embedding as input and semantic feature of corpus as output. Third, bidirectional long short‐term memory with conditional random field (BiLSTM + CRF) is conducted after concatenation of features from knowledge‐attention and multi‐scale CNN. Finally, we compare our results with other baselines. The experimental results show that our proposed method shows promising result in extracting multi‐scale event causality.
Two-View Mammographic Mass Retrieval Using Deep Hashing

Breast cancer is the most commonly diagnosed cancer among women all over the world. Content-based mammographic mass retrieval can assist radiologists to retrieve biopsy-proven masses content similar to the diagnostic ones, which can help radiologists to improve the diagnostic performance. However, although screening mammograms usually consists of two-view (mediolateral oblique view and craniocaudal view) mammography of the same breast, most breast computer-aided diagnosis(CAD) methods incorporated with content-based image retrieval (CBIR) techniques are based on a single-view principle where query mass within a view is analyzed independently. With the advance of deep neural networks in image representation, the deep hashing technique for CBIR maps images into binary feature vectors, which can greatly reduce time and memory costs. In this paper, according to our previous studies, we propose a two-view mammographic mass retrieval approach based on deep hashing. In our retrieval paradigm, a convolutional neural network(CNN) is used to learn both image representation and hash function based on paired labels and class labels. The last layer of CNN will output the image binary codes. Subsequently, the learned binary codes of mass pair are used for matching and ranking within a traditional query-by-example(QBE) framework. Using a dataset that contains a total of 3000 masses collected from the public Digital Database for Screening Mammography(DDSM), this method can yield a retrieval accuracy of 0.8975, which demonstrates its encouraging performance.
Rotation Invariant Convolutional Neural Network Based on Orientation Pooling and Covariance Pooling

Convolutional neural networks (CNNs) have shown promising performance in a variety of visual tasks. However, by using convolution operations, CNNs have limited ability to handle orientation changes of input images. To alleviate this problem, data augmentation is typically used. However, this solution brings additional computational and storage costs and has no sufficient theoretical guarantees on rotation invariance. Alternative solutions have been proposed which only take the first-order features into consideration, without utilizing the higher-order information for representation learning. In this paper, we propose an end-to-end rotation invariant CNN (RICNN) based on orientation pooling and covariance pooling to classify rotated images. Specifically, we learn deep rotated filters to extract rotation invariant feature maps by using two types of orientation pooling (OP), including max OP and average OP. Furthermore, we employ covariance pooling to extract rotation invariant hierarchical second-order features. Experiments on two datasets demonstrate the effectiveness of RICNN for rotation invariant image classification.
Analysis of Reinforcement Learning in Autonomous Vehicles

This paper takes a look at the mechanisms by which autonomous vehicles operate: reinforcement learning in particular. Machine learning techniques used by companies such as Waymo, Tesla, and GM have allowed them to produce high end autonomous driving systems. This paper reviews the reinforcement learning techniques and algorithms used by these companies and proposes alternative solutions to the problems faced by most of their vehicles. Moreover, this paper contains an in-depth analysis of the Q learning technique used by autonomous vehicles.
Contact Tracing Application for Aviation- A Digital Inoculation

Covid-19 has changed the dynamics of how we live and conduct our daily lives. There are more restrictions than ever before and people have to be mindful of the COVID safe protocols to ensure that there are no further infections. Even with protective measures, immunity is not guaranteed. Therefore, contact-tracing mechanisms are of utmost importance to reduce further chained infections. Contact tracing is a containment strategy, which is perfected through digital innovation. This paper analyses current implementations of contact tracing software for air travel. It further elaborates challenges and opportunities with the implementations of digital contact tracing. Finally based on the findings and assessments, the research builds and proposes a novel contact tracing software primarily for the aviation industry. The application capitalizes on the use of a boarding pass to record passenger movement and contacts. Additionally, it exhibits future scope and recommendations for the successful implementation and adoption of digital contact tracing software. The primary beneficiaries of this article are academics, contact tracing software developers, airline and airport operators, immigration, border security and health authorities, government and policymakers, and airline passengers.
Design and Implementation of RFID-Based Chronometer System

This paper combines C# programming, RFID technology and database technology to design a set of automatic timing system for middle and long distance running software. The system is composed of many subsystems, such as information management of test students, reading and management of RFID card, automatic timing processing and statistical analysis data. It can realize the identification, reading and automatic timing of the test students who wear RFID card, and upload the timing data to the database to save and export, and get the test results of the students, realizing automation. Compared with manual timing, can save a lot of manpower and material resources; Compared with the general carpet antenna automatic timing, the equipment is simpler and the cost is lower. Moreover, the error of the test results can also be within a required range, which can meet the basic test requirements of middle and long-distance running, and is suitable for military physical fitness testing and training of middle and long-distance running in colleges and universities.
MING: An interpretative support method for visual exploration of multidimensional data

Dimensionality reduction enables analysts to perform visual exploration of multidimensional data with a low-dimensional map retaining as much as possible of the original data structure. The interpretation of such a map relies on the hypothesis of preservation of neighborhood relations. Namely, distances in the map are assumed to reflect faithfully dissimilarities in the data space, as measured with a domain-related metric. Yet, in most cases, this hypothesis is undermined by distortions of those relations by the mapping process, which need to be accounted for during map interpretation. In this paper, we describe an interpretative support method called Map Interpretation using Neighborhood Graphs (MING) displaying individual neighborhood relations on the map, as edges of nearest neighbors graphs. The level of distortion of those relations is shown through coloring of the edges. This allows analysts to assess the reliability of similarity relations inferred from the map, while hinting at the original structure of data by showing the missing relations. Moreover, MING provides a local interpretation for classical map quality indicators, since the quantitative measure of distortions is based on those indicators. Overall, the proposed method alleviates the mapping-induced bias in interpretation while constantly reminding users that the map is not the data.
Tampering Localization Using Divergence in First Digit Probability Distribution

Passive image forensic techniques investigate fingerprints left by tampering operations. Tampering to a compressed image needs multiple re-compression. Forensic investigation of compressed images involves analysis of quantization fingerprints applied during compression. Re-compressed images show different First Digit Probability Distribution (FDPD) when DCT compression grids of first and later compression are not aligned with each other. Often such images possess fingerprints of first compression. Re-compressed images, having first and re-compression DCT grids aligned with each other, share different FDPD. This paper addresses a technique that uses this divergence in FDPD for locating tampered regions. The divergence in FDPD is used to train the SVM Classifier. Our proposed technique assumes that the tampered region is very small and compression grids will not overlap in that region. Unlike the other techniques, our algorithm does not need pre-computed parameters for training classifiers. Hence, the performance of the SVM classifier is not dependent on the specific compression quality of an image. Algorithm itself evaluates the FDPD model parameters and trains the classifier as per the requirement of the test image.
A metric for quantifying nonlinearity in <i>k</i>-dimensional complex-valued functions

Modeling and simulation is a proven cost-efficient means for studying the behavioral dynamics of modern systems of systems. Our research is focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is required to quantify the amount of NL present in the complex-valued function. In this paper, we introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is an extension of a real-valued NL metric into the k-dimensional complex domain. The metric is flexible as it uses discrete input–output data pairs instead of requiring closed-form continuous representations for calculating the NL of a function. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k-dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between zero and one. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.
A Novel Approach for Smart Contracts Using Blockchain

Despite all the advantages delivered by smart contracts deployed on top of the blockchain, several challenges are hitting the industry. Blockchain has many security and performance issues that need attention. It facilitates the interaction between two parties who can interact without a third party to accept this transaction. This leads to the creation of smart contracts that help the blockchain to execute more efficiently. A smart contract is an executable code that runs on top of the blockchain. Any modification after execution is not allowed. This causes a problem that is not solved by many scientists. This paper proposes a solution to this problem by creating a modifying blockchain that will hold all the modified data to be added to the smart contract using Ethereum. The proposed solution allows the modification to happen in a separate blockchain associated with the main blockchain containing the smart contract. This will result in faster execution, easier modifying and less used storage. Ethereum used to deploy the blockchain while deploying the smart contract requires a programing language that supports all the necessary requirements for deployment. Solidity programing language is used as it provides all the functions and operations for creation.
ПАРАДИГМЫ ТЕХНОЛОГИЧЕСКОГО РАЗВИТИЯ КАК БАЗИСНОЕ ПОНЯТИЕ СОВРЕМЕННОЙ ФИЛОСОФИИ ТЕХНИКИ И ТЕХНОЛОГИИ (Paradigms of Technological Development as a Basic Concept Modern Philosophy of Engineering and Technology)

The article deals with the problem of applying the well-known concept of scientific paradigm to the field of scientific and technical developments. Scientific and technical advancement, its meaning and orientation are the subject of numerous discussions today. The concept of a technological paradigm, which is often found in modern works in the field of philosophy of engineering and technology, is too general in nature, which hardly allows it to be used as an analytical concept for a better understanding of the problems of modern scientific and technological development. Technologies are advancing today in many ways different directions, and the approach from the point of view of a single paradigm and paradigm shift (technological revolutions) gives an insufficiently differentiated picture.